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User Manual - Network Workbench

1. update update eclipse core core commands_3 3 0 I20070605 0010 jar contenttype 3 2 100 v20070319 jar core databinding 1 0 0 120070606 0010 jar core databinding beans 1 0 0 120070606 0010 jar core expressions 3 3 0 v20070606 0010 3Jjar core jobs_3 3 0 7v20070423 jar core net 1 0 0 120070531 jar core core eguinox eguinox eguinox eguinox eguinox eguinox eguinox eguinox eguinox metatype 1 0 0 v20070226 jar eguinox eguinox eguinox eguinox eguinox runtime 3 3 100 v20070530 Tar runtime compatibility a th 3 2 100 v20070502 app IOI R33x 120070628 Jar cm 3 2 Us vZ20070116 jat common 3 3 0 v40070426 Jar ds 1 0 0 720070226 jar event_1 0 100 v20070516 Jar launcher 1 0 0 v20070606 jar launcher 1 0 1 R33x v20080118 jar launcher gtk linux x86_1 0 0 7v20070606 log_1 0 100 7v20070226 jar preferences 3 2 101 R33x v20080117 jar registry 3 3 1 R53 vZ00 70602 Jar useradmin 1 0 0 v20070226 jar util_1 0 0 200803111100 jar help 3 3 0 v20070524 jar jface 3 SU 52007000600 L0 ja E jJface databinding 1 1 0 120070606 0010 Jar osgi_3 3 2 R33x_v20080105 jar osg1 services 3 1 200 720070605 jar osgi util_3 1 200 7v20070605 jar EOD Jas swt_3 3 swt gtk 0 v20070612 jar 0 v3346 jar linux x86_3 3 0 v3346 jar ui_3 3 0 I20070614 0800 jar ui forms_3 3 0 7v20070511 jar ui workbench_3 3 0 I120070608 1100 jar update update Configurator_3 2 100 7v200
2. Bibtex bib 10 e Endnote Export Format enw and the following network file output formats GraphML xml or graphml Pajek MAT mat Pajek NET net NWB nwb XGMML xml These formats are documented at https nwb slis indiana edu community n DataFormats HomePage 3 Code Library The NWB Tool is an empty shell filled with plugins Some plugins run the core architecture see OSGi and CIShell plugins Others convert loaded data into in memory objects into formats the different algorithms can read behind the scenes Most interesting for users are the algorithms plugins that can be divided into algorithms for preprocessing analysis modeling and visualization Last but not least there are other supporting libraries and entire tools that are plugged and played 3 1 OSGi Plugins org org org org org org org org org org org org org org org org org org org org org org org org org org org org org org org org org org org org org org org 3 2 ClShell Plugins eclipse eclipse eclipse eclipse eclipse eclipse eclipse eclipse eclipse eclipse eclipse eclipse eclipse eclipse eclipse eclipse eclipse eclipse eclipse eclipse eclipse eclipse eclipse eclipse eclipse eclipse eclipse eclipse eclipse eclipse eclipse eclipse eclipse eclipse eclipse eclipse eclipse eclipse
3. GUESS Display gt Information Window reveals detailed information for any node or edge Here the node with the highest GCC value was selected Alternatively nodes could have been color and or size coded by their degree using e g gt g computeDegrees gt colorize outdegree gray black Note that the outdegree corresponds to the LCC within the given network while the indegree reflects the number of references helping to visually identify review papers The complete paper paper citation network can be split into its subnetworks using Analysis gt Unweighted amp Directed gt Weak Component Clustering with the default values The largest component has 163 nodes the second largest 45 the third 24 and the fourth and fifth have 12 and 11 nodes respectively The largest component also called giant component is shown in Figure 7 The top 20 papers by times cited in ISI have been labeled using gt toptc g nodes gt def byte nl n2 are return omp nl a globalcitationcount n2 globalcitationcount toptc sort bytc gt toptc reverse gt toptc gt for i in range 0 20 49 toptc i labelvisible true Alternatively run GUESS File gt Run Script and select kyournwbdirectory sampledata scientometrics isi paper citation nw py SKO e ra 2004 PROC NAT ACAD SCI USA ET pa Sia Aa KERERIGMBAV IIPS g VIES Figure 7 14 Giant components of the paper citation networ
4. GUESS supports the repositioning of selected nodes Multiple nodes can be selected by holding down the Shift key and dragging a box around specific nodes The final network can be saved via GUESS File gt Export Image and opened in a graphic design program to add a title and legend The image below was created using Photoshop and label sizes were changed as well Joint Co Authorship Network Stanley Wasserman j A TIC PF rie gt I NN La Y JAS FA Ep P Vespignani rt elem ikl BR Node Size amp Color Edge Size amp Color Number Ol Papers Number ot limes Lo Authored 127 Eugene Garfield 33 35 5 1 SAN SN 1 127 1 33 Figure 7 15 Undirected weighted co author network for FourNetSciResearchers dataset 7 6 2 2 Word Co Occurrence Network The topic similarity of basic and aggregate units of science can be calculated via an analysis of the co occurrence of words in associated texts Units that share more words in common are assumed to have higher topic overlap and are connected via linkages and or placed in closer proximity Word co occurrence networks are weighted and undirected In the NWB Tool select the table of 361 unique ISI records from the FourNetSciResearchers dataset in the Data Manager Run Preprocessing gt Normalize Text using parameters New Separator 22 Abstract Check this box The performed text
5. Wilson C S 2001 Informetrics In M E Williams Ed Annual Review of Information Science and Technology Vol 37 pp 107 286 Medford NJ Information Today Inc American Society for Information Science and Technology License The Network Workbench Tool NWB Tool is licensed under the Apache License Version 2 0 the License You may obtain a copy of the License at http www apache org licenses LICENSE 2 0 Unless required by applicable law or agreed to in writing software distributed under the License is distributed on an AS IS BASIS WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND either express or implied See the License for the specific language governing permissions and limitations under the License 11
6. raio E a mu i j da Wardeneot BH Dias M P Wies gii Deangelis R W ea Kautenan H z Marsili M Evean ngn ee Morail Mi E dago F ll digas laj i iiaj SI ipa l AN sort Papa Q 5 Mi vena A i ri j j j j j E Ain pa n R Pabia J 2 WA Cha A Aa F IS asi A Un LLE CI 1988 2002 1988 2007 Figure 7 11 Evolving co authorship network of Vespignani from 1988 2007 The four networks reveal that from 1988 1992 Alessandro Vespignani had one main co author and four other co authors His network expanded considerably to 71 co authors and 221 co author links until 2007 7 4 Geospatial Analysis Geospatial analysis has a long history in geography and cartography Geospatial analysis aims to answer the question where something happens and with what impact on neighboring areas It requires spatial attribute values such as geolocations for authors or their papers extracted from affiliation data or spatial positions of nodes generated from layout algorithms Geospatial data can be continuous 1 e each record has a specific position or discrete 1 e a position or area shape file exists for sets of records e g number of papers per country Spatial aggregations e g merging via zip codes counties states countries continents are common Cartographic generalization refers to the process of abstraction such as 1 graphic ge
7. the background by clicking and holding down the left mouse button and move it using the mouse e Zoom Using the scroll wheel on the mouse OR press the and buttons in the upper left hand corner OR right click and move the mouse left or right Center graph by selecting View gt Center e Click to select move single nodes Hold down Shift to select multiple e Right click node to modify Color etc Use the Graph Modifier to change node attributes e g e Select all nodes in the Object drop down menu and click Show Label button e Select Resize Linear Nodes totalities drop down menu then type 5 and 20 into the From and To Value box separately Then select Do Resize Linear e Select Colorize Nodes totalities then select white and enter a 204 0 51 in the pop up color box boxes on in the From and To buttons e Select Format Node Labels replace default text originallabel with your own label in the pop up box Enter a formatting string for node labels This will create the labels shown in Figure 4 17 25 Yisualization GUESS Pucci Field Value color 255 25 fixed false height 5 0 image label Pucci labelcolor 0 0 0 labelsize 12 labelvi true name ni2 original Pucci priorates O stroke cadetb style 2 totalities 1 visible true Information Window wealth 3 width 5 0 x 15
8. CIShell Framework Applications of Social Network Analysis University of Zurich ETH Zurich Kessler Michael M 1963 Bibliographic coupling between scientific papers American Documentation 14 1 10 25 Kleinberg J M 1999 Authoritative sources in a hyperlinked environment Journal of ACM 46 5 604 632 Kleinberg J M 2002 Bursty and Hierarchical Structure in Streams Paper presented at the 8th ACMSIGKDD International Conference on Knowledge Discovery and Data Mining ACM Press pp 91 101 Kohonen Tuevo 1995 Self Organizing Maps Berlin Springer Kraak Menno Jan Ferjan Ormeling 1987 Cartography Visualization of Spatial Data Delft NL Delft University Press Krebs Valdis 2008 Orgnet com Software for Social Network Analysis and Organizational Network Analysis http www orgnet com inflow3 html accessed Kruskal J B 1964 Multidimensional Scaling A Numerical method Psychometrica 29 115 129 Landauer T K P W Foltz D Laham 1998 Introduction to Latent Semantic Analysis Discourse Processes 25 259 284 Landauer T K S T Dumais 1997 A Solution to Plato s Problem The Latent Semantic Analysis Theory of the Acquisition Induction and Representation of Knowledge Psychological Review 104 211 240 Lenoir Timothy 2002 Quantitative Foundations for the Sociology of Science On Linking Blockmodeling with Co Citation Analysis In John Scott Ed Social Networks Critical Concepts i
9. real world gr 2001 Physical Review E 135 16 88 13 GBianconi ALB Bose Einstein Condensati 2001 Physical Review Letters 132 18 86 14 2ZDezsd AL Bara Halting viruses in scale fr 2002 Physical Review E 106 13 25 15 ALBarab si E R Deterministic scale free n 2001 Physica A Statistical Mecha 105 10 50 16 R Albert H Jeon Internet Diameter of the 1999 Nature i 4 25 3171 Sunday September 21 2008 hb Figure 7 2 Publish or Perish interface with query result for Albert L szl Barabasi To save records select from menu File gt Save as Bibtex or File gt Save as CSV or File gt Save as EndNote All three file formats can be read by NWB Tool The result in all three formats named barabasi is also available in the respective subdirectories in yournwbdirectory sampledata scientometrics and will be used subsequently 7 2 1 4 Comparison of ISI Scopus and Google Scholar A number of recent studies have examined and compared the coverage of Thomson Scientific s Web of Science WoS Scopus Ulrich s Directory and Google Scholar GS It has been shown that the databases have a rather small overlap in records The overlap between WoS and Scopus was only 58 2 The overlap between GS and the union of WoS and Scopus was a mere 30 8 While Scopus covers almost twice as many journals and conferences than WoS it covers fewer journals in the arts and humanities A comprehensi
10. science to scien can be applied Porter 1980 Co word analysis identifies the number of times two words are used in the title keyword set abstract and or full text of e g a paper The space of co occurring words can be mapped providing a unique view of the topic coverage of a dataset Similarly units of science can be grouped based on the number of words they share in common Callon Courtial Turner amp Bauin 1983 Callon Law amp Rip 1986 Salton s term frequency inverse document frequency TFIDF is a statistical measure used to evaluate how important a word is to e g paper In a corpus The importance increases proportionally to the number of times a word appears in the paper but is offset by the frequency of the word in the corpus Salton amp Yang 1973 Dimensionality reduction techniques such as self organizing maps SOM or the topics model see Table 7 1 are commonly used to project high dimensional information spaces e g the matrix of all unique papers times their unique terms into a low typically 2 dimensional space See section 7 6 2 Co Occurrence Linkages for examples on how to use the NWB Tool for word co occurrence analysis Deerwester Dumais Furnas Landauer amp Harshman 1990 Griffiths amp Steyvers 2002 Kohonen 1995 Kruskal 1964 Landauer amp Dumais 1997 Landauer Foltz amp Laham 1998 7 6 Network Analysis The study of networks aims to increase our understanding of natu
11. www endnote com encopyright asp accessed on 7 15 08 Thomson Reuters 2008b Web of Science http scientific thomsonreuters com products wos accessed on 7 17 08 Tobler Waldo R 1973 A Continuous Transformation Useful for Districting Science 219 215 220 Topter F 1974 Kartographische Generalisierung Gotha Leipzig VEB Herrmann Haack Geographisch Kartographische Anstalt Topfer F W Pillewizer 1966 The Principles of Selection Cartographic Journal 3 10 16 Wasserman S K Faust 1994 Social network Analysis Methods and Applications New Y ork Cambridge University Press Watts D J S H Strogatz 1998 Collective dynamics of small world networks Nature 393 440 442 Wellman B Howard D White N Nazer 2004 Does Citation Reflect Social Structure Longitudinal Evidence from the Globenet Interdisciplinary Research Group JASIST 55 111 126 White Howard D Katherine W McCain 1998 Visualizing a Discipline An Author Co Citation Analysis of Information Science 1972 1995 Journal of the American Society for Information Science 49 4 327 355 Wikimedia Foundation Inc 2009 PageRank http en wikipedia org wiki PageRank accessed on 8 31 2009 Wikimedia Foundation Inc 2009 Poisson Distribution http en wikipedia org wiki Poisson distribution accessed on 8 3 1 2009 Williams Thomas Colin Kelley 2008 gnuplot homepage http www gnuplot info accessed on 7 17 08 76
12. Bis pk yew Fontes Jok pep E Convert Pi Select Gis Ede yew Fgrortes jos pep GP de usr ed Sar Os bateo loro ref ee e de Se nse Awad saach 50 e REE Mataia Ard IO E National Science Foundation in P p Md UJ Search Reset HOME FUHDIKO DISCOVERIES HEWZ PUBLICATIONS STATISTICS ABOUT Fastlane Search Resull Back Award Search A A Eanulbi ir iomad by id dita with th mani renant kabrida at Eka tap Glick an 4 sluma basdag to ie kort th kiuli The up ed aras ar rha rigkr af sagh ralemn nula rarsral whashar rha sort it aucancoso ar sanranging karim jor Orange reo rerantima emch Aii Frer Frei 7 a p 7 Te view The abunraon click on the rr sumbar or pale Click pa Da dara im other caumas po perform oa naw ch with TR ee i Szigprgh ELP awards hund sesplaping 1 bo 30 Hint Tha bios ald balio Sanreh Add Lor panrokaj cha kiela TAE ked beard dumber falda Fit Pei 1 34 3 0 8 8 10 01 13 13 Max fLart Seth e Pot Ravtrict to Title Daha IO A Awardes Information Prince Erremirtigabor pj PILockup Haki Ersiloediaj CEPE sall iion in ale dard had Diui 00 P Js Organization Lookup Figure 7 3 NSF Award Search site To retrieve all projects funded under the new Science of Science and Innovation Policy SciSIP program simply select the Program Information tab do an Element Code Lookup enter 7626 into Element Code field and
13. Figure 7 25 Co authorship network of FourNetSciResearchers in CiteSpace II 7 8 3 3 Word Co Occurrence Network Figure 7 26 shows the CiteSpace rendering of the keywords descriptors and identifiers co occurrence network for the FourNetSciResearchers dataset using ten 2 year time slices covering 1988 2007 The top 50 of the most often 68 occurring keywords from each slice are selected in the network which has 247 nodes and 830 links Compare to NWB rendering in section 7 6 2 2 Word Co Occurrence Network if citation analysis Figure 7 26 Keyword co occurrence network of FourNetSciResearchers in CiteSpace II 7 8 3 4 Burst Detection Burst detection using NWB Tool was discussed in section 7 3 2 Burst Detection ie a citec references ki LU z l CRS Russianicad Sci Sig f l A Inst Sci Informat adas oal proponos stems diu E sighted net brks complex sys fracial dimension good agreement PA a orand E Eotvos Univ Ng KA erto U Tama li i Ari US Univ hen 4 7 i sss EN secang W ion vig ld e a A scale fi NO tre Dame li me mi Phys 4 Ko bay San rae ane grevisiae Vj No ne Fines For cll LOG okra bsprbing gt da bero NINJ i Ada i im Ffanserintionakregulatory si e av i ing states aschonehla oolj A UNIV LL socla networks A noj flee en kasigi N UNIV LL AS Ve AT OSTON E Rel asses ZN moj umaliza mup Ve i TEO Lang
14. datasets downloaded from ISI Thomson Scientific models parameter and data files needed for TARL model nsf datasets downloaded from the National Science Foundation properties parameter files needed for network extraction scopus datasets downloaded from Scopus Relevant algorithm plugins are stored in the general nwb plugins directory see section 3 Code Library Each algorithm has the package name that best fits its function e g preprocessing analysis visualization etc An example is edu iu nwb preprocessing cocitationsimilarity_1 0 0 In the NWB Tool the scientometrics specific algorithms have been grouped under a special Scientometrics menu item to ease usage see Figure 7 1 31 a Network Workbench Tool File Preprocessing Modeling Analysis Visualization Scientometrics Help E Console Remove ISI Duplicate Records Remove Rows with Multitudinous Fields SU Extracted Co Authorship Ni T Graph and Network Ani FE Author information HISI Data katyiPaper 08 Nw 3H 361 L Unique ISI Records ZS Extracted Co Authorship Ni T Graph and Network Ani Jetwork Analysis Toolkit NAT was selected Detect Duplicate Node Implementer s Timothy Kelley Update Network by Integratoris Timothy Kelley Reference Robert Sedgewick Algorithms in Java Third 0 201 31663 3 Section 19 3 pp 205 Documentation https mwb slis indiana edu comn s graph claims to be undirected w ergin g od E
15. nwb nwb nwb nwb nwb nwb nwb nwb nwb nwb nwb nwb nwb nwb nwb nwb analysis nwb analysis nwb nwb nwb nwb nwb nwb nwb nwb nwb nwb nwb nwb nwb nwb nwb nwb nwb nwb nwb analysis analysis analysis analysis analysis analysis analysis analysis analysis analysis analysis analysis analysis analysis analysis analysis analysis analysis analysis analysis analysis analysis analysis analysis analysis analysis analysis analysis analysis analysis analysis analysis analysis totaldegreedistribution_1 0 0 jar cransItivIty adjacency 1 0 0 Jar weakcomponentclustering_1 0 0 jar weighted weighted weighted weighted weighted weighted weighted weighted analysis analysis analysis analysis analysis analysis analysis analysis analysis analysis analysis ke a dE Java Java java java java java averageshortestpath 1 0 0 jar blondelcommunitydetection 0 0 burst_1 0 0 jar Clustering_1 0 0 jar Clustering_vs_k_1 0 0 jar connectedcomponents 1 0 0 jar diameter dichotomize 1 0 02 jar 1 jar LO Tar extractattractors_0 0 1 jar extractcoauthorship_1 0 0 jJar extractdirectednetfromtable 1 0 0 jar extractnetfromtable 1 0 0 jar hits_1 0 indegreedistribution 1 0 0 jar isidupremover 0 0 1 jar isolates directedknn 0 0 1 jar nodedegree 0 0 1 jar nodeindeg
16. of many data formats BibExcel 2006 Scientom Transforms bibliographic data Graphical Persson into forms usable in Excel 2008 Pajek NetDraw and other programs NWB Tool 2006 Bio Network analysis amp Graphical SocSci visualization tool conducive Publish or 2007 Scientom Data Harvests and analyzes data Web based Perish Harzing Collection from Google Scholar 2008 and Analysis focusing on measures of research impact Many of these tools are very specialized and capable For instance BibExcel and Publish or Perish are great tools for bibliometric data acquisition and analysis HistCite and CiteSpace each support very specific insight needs from studying the history of science to the identification of scientific research frontiers The S amp T Dynamics Toolbox provides many algorithms commonly used in scientometrics research and it provided bridges to more general tools Pajek and UCINET are very versatile powerful network analysis tools that are widely used in social network analysis Cytoscape is excellent for working with biological data and visualizing networks The Network Workbench Tool has fewer analysis algorithms than Pajek and UCINET and less flexible visualizations than Cytoscape Network Workbench however makes it much easier for researchers and algorithm authors to integrate new and existing algorithms and tools that take in diverse data formats The OSGi http www osgi org component architec
17. 010072703 0 009628947 0 00906183 0008962325 00087675741 Paper Barabasi Aly 1999 SCIENCE V286 P509 Watts DJ 1998 NATURE V393 P440 Amaral LAN 2000 P NATL ACAD SCI USA V97 P11149 Albert R 2002 REV MOD PHYS V74 P47 Faloutsos M 1999 COMP COMM R V29 P251 Pastorsatorras R 2001 PHYS REV LETT V86 P3200 Albert R 2000 NATURE V406 P378 Erdos P 1960 PUBL MATH I HUNG V5 P17 Barabasi AL 1999 PHYSICA A V272 P173 Albert R 1999 NATURE V401 P130 Figure 7 21 shows a GUESS visualizations of the giant component of the FourNetSciResearchers dataset Nodes are area size coded and color coded according to their authority_score left and hub_score values right Top 10 nodes with authority_score and hub_score are highlighted with purple and red respectively k kla ay pi b 3 dn KI 5 eee y a G F3 oF a a al EK Ci ki F la E x Y j TAJ LA a adi li ma i ae D koro eaj ki d 4 repa i EE ALE A i mei e tt ea L js A E gt PA TA tes a gt ae we an i FA JN pd o oa mi 4 wi ha ar a Figure 7 21 Giant component of the paper citation network of FourNetSciResearchers dataset with nodes size coded according to their authority_score value left and hub_score right As can be seen all nodes with high HITS value are in the center of the network The top 10 nodes with high authority_score are different from the nodes with high hub_score 7 7 3 3 PageRank
18. 200 200 200 0 0 0 resizeLinear weight 25 8 colorize weight 127 193 65 255 black for n in g nodes n strokecolor n color toptc g nodes gt def bytc nl n2 aaa return cmp nl globalcitationcount n2 globalcitationcount gt toptc sort bytc VVVNVV V 54 gt toptc reverse gt toptc gt for i in range 0 20 toptc i labelvisible true Alternatively run GUESS File gt Run Script and select yournwbdirectory sampledata isi reference co occurence nw py Figure 7 17 Reference co occurrence network layout for FourNetSciResearchers dataset 7 6 3 Co Citation Linkages Two scholarly records are said to be co cited if they jointly appear in the list of references of a third paper see Figures 7 12 and 7 18 right The more often two units are co cited the higher their presumed similarity 7 6 3 1 Document Co Citation Network DCA DCA was simultaneously and independently introduced by Small and Marshakova in 1973 Marshakova 1973 Small 1973 Small amp Greenlee 1986 It is the logical opposite of bibliographic coupling The co citation frequency equals the number of times two papers are cited together 1 e they appear together in one reference list In the NWB Tool select the paper citation network see section 7 6 1 1 Paper Paper Citation Network and run Scientometrics gt Extract Document Co Citation Network The co citation network will bec
19. Extract Directed Network Extract Paper Citation Network Extract Author Paper Network lodes 51 Extract Co Occurrence Network Me Isolated nodes O gt Weak Component Clust lode attributes present label numberOfWorks Extract Word Co Occurrence Network SS Weak Component Clust does 174 Extract Co Author Network ES Weak Component Clust LEN Weak Component Clust Author information 2 lo self loops were discovered lo parallel edges were discovered Extract Reference Co Occurrence Bibliographic Coupling Network E tract acurnment C i i Hatar Neti OFE BE scheduler From List Remove completed automatically Remove all completed mi Wl Network Analysis Toolkit wd O7 16f2008 07 47 23 PM o A Extract Co Authorship Ne 0711612008 07 47 17 PM Liewrouw LAI WA E Load and Clean ISI File 0716 2008 07 46 40 PM amen CSET Rockwell HE al E Network Analysis Toolkit 07 16 2008 07 42 20 PM amader EA Granito CE L a lana ia Extract Co Authorship Ne 07 16 2008 07 42 10 PM lamer an EA KI Load 07 16 2008 07 37 43 PM Figure 7 1 Scientometrics menu in the Network Workbench Tool 7 1 2 General Workflow The general workflows of scientometric studies and specifically the mapping of science were detailed in B rner Chen amp Boyack 2003 The general workflow of a scientometric study is given in Table 7 1 Major steps are 1 data extraction 2
20. GUESS visualization with nodes size coded and color coded according to their BC values using the following GUESS interpreter commands resizeLinear sitebetweenness 2 40 nodes colorize sitebetweenness 200 200 200 0 0 0 colorize numberofcoauthoredworks 127 193 65 0 0 0 edges resizeLinear numberofcoauthoredworks 5 8 gemLayout binPackLayout VVVV VV The elimination of the top 5 nodes with the highest BC values leads to unconnected clusters of nodes see Figure 7 20 right Note how Garfield s network top right disintegrates into many smaller subnetworks while the joint Barabasi Vespignani network exhibits a giant connected component even after the removal of the top 4 edges The Wasserman network bottom right 1s unaffected since no nodes or edge have been removed 59 Figure 7 20 Layout of FourNetSciResearchers dataset with nodes size coded according to their BC value left and with the top 5 nodes with the highest BC values removed right 7 7 3 2 Hyperlink Induced Topic Search HITS HITS is a widely used algorithm in Web structure mining developed by Jon Kleinberg Kleinberg 1999 It is most often used for rating web pages with an authority score and a hub score The authorityscore measures the value of a web page s page content and its hub score measures the value of hyperlinks between pages Authority and hub scores are mutually recursive authority is determined by how many hub
21. Hampton NCSA is an open source extensible software platform which supports seismic risk assessment based on the Mid America Earthquake MAE Center research in the Consequence Based Risk Management CRM framework Elnashai et al 2008 It uses the Eclipse Rich Client Platform RCP that includes Equinox a component framework based on the OSGi standard The 125 MAEviz plugins consist of 6 core plugins 7 plugins related to the display of hazard building and bridges and lifeline data 11 network and social science plugins and 2 report visualization plugins Bard previously NCSA GIS has 11 in core plugins 2 relevant for networks and 10 for visualization The analysis framework has 6 core plugins Ogrescript has 14 core plugins A total of 54 core Eclipse OSGI plugins are used such as org eclipse core org eclipse equinox org eclipse help org eclipse osgi org eclipse ui and org eclipse update https wiki ncsa uiuc edu display MAE OSGI Plugins e TEXTrend http www textrend org lead by George Kampis E tv s University Hungary develops a framework for the easy and flexible integration configuration and extension of plugin based components 65 in support of natural language processing NLP classification mining and graph algorithms for the analysis of business and governmental text corpuses with an inherently temporal component Kampis Gulyas Szaszi amp Szakolczi 2009 TEXTrends recently adopted OSG1 CIShell for th
22. Help contents Results Papers 111 Cites paper 129 21 h index 30 AWCR 1711 35 Copy statistics What s new Citations 14342 Citesfauthor 7115 79 g index 111 AW index 41 37 Version information Years 36 Papersfauthor 58 33 he index 25 AWCRpA 850 69 Copy results Cites year 398 39 Authors paper 2 56 hl index 10 84 hI norm 21 E Cites Per R Authors Tite Year Publication E 3899 389 90 1 ALBarab si R Al Emergence of Scaling in R 1999 Science Check selection 3146 449 43 2 RaAlbert AL Bar Statistical mechanics of c 2002 Reviews of Modern Physics 1943 138 79 3 ALBarab si HE Fractal Concepts in Surfa 1995 Uncheck all 1341 223 50 4 Al Barab si RE Linked The New Science 2003 American Journal of Physics pers 514 57 11 5 RAlbert AL Bar Topology of Evolving Net 2000 Physical Review Letters 444 74 00 7 ERavasz AL Bar Hierarchical organization i 2003 Physical Review E Uncheck selection 439 43 90 6 AL Barab si RAI Emerging of scaling in ran 1999 Science 381 42 33 8 AL Barabasi R Al Scale free characteristics 2000 Physica 4 Statistical Mecha Help 198 33 00 9 AL Barab si Linked How Everything I 2003 163 13 58 10 IDaruka AL Bar Dislocation Free Island Fo 1997 Physical Review Letters 160 11 43 11 R Cuerno AL Ba Dynamic Scaling of Ion Sp 1995 Physical Review Letters 136 17 00 12 IJ Farkas IDere Spectra of
23. Macmillan Carrington P J Scott S Wasserman 2005 Models and Methods in Social Network Analysis New York Cambridge University Press Chen Chaomei 1999 Visualizing semantic spaces and author co citation networks in digital libraries Information Processing and Management 35 3 401 420 Chen Chaomei 2006 CiteSpace II Detecting and Visualizing Emerging Trends and Transient patterns in Scientific Literature JASIST 54 5 359 377 Cisco Systems Inc 2004 Network Analysis Toolkit http www cisco com univercd cc td doc product natkit index htm accessed on 7 15 08 Csardi Gabor Tamas Nepusz 2006 The igraph software package for complex network research http necs1 org events iccs6 papers c1602a3c126ba822d0bc4293371c pdf accessed on 7 17 08 Cyberinfrastructure for Network Science Center 2008 Cyberinfrastructure Shell http cishell org accessed on 7 17 08 Cytoscape Consortium 2008 Cytoscape http www cytoscape org index php accessed on 7 15 08 Davidson G S B N Wylie Kevin W Boyack 2001 Cluster Stability and the Use of Noise in Interpretation of Clustering IEEE Information Visualization pp 23 30 San Diego CA IEEE Computer Society de Moya Anegon Felix Zaida Chinchilla Rodriquez Benjamin Vargas Quesada Elena Corera Alvarez Francisco Jos Munoz Fernandez Antonio Gonzalez Molina Victor Herrero Solanao 2007 Coverage Analysis of Scopus A Journal Metric Approach Scien
24. Manager The first is a table with all ISI records The second is a derived indented table with unique ISI records named 262 Unique ISI Records In the latter file ISI records with unique ID numbers UT field are merged and only the record with the higher citation count CT value is kept Select the 262 Unique ISI Records table and run Analysis gt Textual gt Burst Detection using the parameters Gamma 140 default setting General Ratio 2 0 default setting First Ratio 2 0 default setting 1 Bursting States default setting Date Column Select Publication Year from the long listing Date Format Select yyyy Text Column Select Authors Text Separator l Note Throughout the tutorial we will use to indicate comments or further instructions within parameter or code blocks These do not need to be entered into the tool A third table derived from 262 Unique ISI Records labeled Burst detection analysis will appear in the Data Manager On a PC running Windows right click on this table and select view to see the data in Excel On a Mac or a Linux system right click and save the file then open using the spreadsheet program of your choice The table has 6 columns The first column lists bursting words here author names the length of the burst the burst weight burst strength together with the burst start and end year Note that words can burst multiple times If they do t
25. and Fruchterman Rheingold and Small World Layouts Specified layout requires pre computed x y values for each node Node positions can be computed e g using Visualization gt DrL Visualizations of the yournwbdirectory sampledata networks florentine nwb network using Radial Tree Graph Force Directed with Annotation and Fruchterman Rheingold with Annotation are given in Figure 4 14 Note that algorithms that do not read nwb format e g Balloon Graph are grayed out and not selectable Tornabuoni Ez Castellani x Lamberteschi Edge Color marriage Node Shape priorates Node Size wealth Node Color totalties Mr O oo 30 l 1450 10 IN sso Figure 4 14 Radial Tree Graph and Fruchterman Rheingold with Annotation prefuse layouts The Fruchterman Rheingold and the Force Directed with Annotation layout were run using the parameter values shown in Figure 4 15 left The menu to the right of the Force Directed layout lets one increase the DefaultSpringLength to spread out nodes ad Force Directed with Annolabon ture kurbaj Y e Salviati Barmastutihata a p ENI Fa DragForos Pazzi O Zl a j Fi as l Fi Kopna SN Select the property labels NOTE Node and Edge properties may appear together and not all properties are suitable for all values SpriregForce Numeric properties work in all cases and non numeric properties M e dic l i 3 he F
26. assumed to reflect topic similarity Co occurrence networks are undirected and weighted In NWB Tool a bibliographic coupling network is derived from a directed paper citation network see section 7 6 1 1 Paper Paper Citation Network Select the paper citation network of the FourNetSciResearchers dataset in the Data Manager Run Scientometrics gt Extract Reference Co Occurrence Bibliographic Coupling Network and the bibliographic coupling network becomes available in the Data Manager Running Analysis gt Network Analysis Toolkit NAT reveals that the network has 5 335 nodes 5 007 of which are isolate nodes and 6 206 edges Edges with low weights can be eliminated by running Preprocessing gt Extract Edges Above or Below Value with parameter values MA Extract Edges Above or Below Yalue x Extract all edges with an attribute above a certain number Extract from this number 4 0 iJ T Below nd Mumeric Attribute weight ball ee Cancel Isolate nodes can be removed running Preprocessing gt Delete Isolates The resulting network has 241 nodes and 1 508 edges in 12 weakly connected components This network can be visualized in GUESS see Figure 7 17 and the above explanation Nodes and edges can be color and size coded and the top 20 most cited papers can be labeled by entering the following lines in the GUESS Interpreter resizeLinear globalcitationcount 2 40 colorize globalcitetioncount
27. atin naj rar iem rra deviis nn Medel thaw dere 19 37 1007 pi ADTs htigri fela ulin Gree eth h aly paper fi ulo gel Acknowledgements Fie ida Da i hired Bey thon heel of Library a d Peda ga Po detoj The Gea nahan Pir a k A defer al foliara Mikeno aye them Hate ri Fes rod gtiers iras dati m I OTINTAJ sad 138 O319896 and a jarmi Myf Peer alien earel i arma p dema skrang E prela Almera da i ae Saat Her Pi d mani dai Para an Foil artoj ts fl al A tha teal are Woh el Aa ni ar he AA F rih e aan ha nena af ia Haiba Air Majn silu l Sy IDEAR ANDARE koj Ey SOMOOL OF LIBRARY AND INFORMATION SCIENCE Ta Dana 2 Do you want Firefox to remember this password Remember Never for This Site Not Now Scholarly Database Search Mozilla Firefox File Edit View History Bookmarks Tools Help Ku x A E http sdb slis indiana edu search LY HG Google Most Visited Pe Getting Started Latest Headlines SCHOLARLY DATABASE Cyberinfrastructure for Network Science Center SLIS Indiana University Bloomington Search Edit Profile About Logout If multiple terms are entered in a field they are automatically combined using OR So breast cancer matches any record with breast or cancer in Creators that field l You can put AND between terms to combine with Title AND Thus breast AND cancer would only match records that contain b
28. can put AND between terms to combine with AND Thus breast AND cancer would only match records that contain both terms Double quotation can be used to match compound terms e g breast cancer retrieves records with the phrase breast cancer and not records where breast and cancer are both present but not the exact 38 phrase The importance of a particular term in a query can be increased by putting a and a number after the term For instance breast cancer 10 would increase the importance of matching the term cancer by ten compared to matching the term breast fa Penta De sheer Wel Stole Hirrien Mu Kobo ee Heey Bookmarks Book Heb B E Xu O resistance saj run AR Most Vine IIe Getting Started oo Latent Headlines E De pau ant Fetes Lo remeber th parce SCHOLARLY DATABASE K2yherinfraninuciura dor Herwork Science Gentar SUS Indiana Uneerury Woman IU User Non IU User lU inert Feat lajon a Tisa Carte al Drail Su iton anlar irrita ZA Sia rd LA ara Gy ern Phe diajn on bats adie be fra iibe Tij ta Wh aija Pigi tO to ILI Login hot Registered Wet Espai e oe lij eee Bagster eo rion Iii liam Ln the mermi here John 2008 Gemeep Theory Bebo 435 ki Pete Picate Cite As kiu Apri farir Bree ima Era Jain Em wia a birar atp IO Y The bli Er sd a Lada dee ipartar amar ju ives ida ai iua ah esis
29. cishell testing convertertester algorithm ConverterTesterAlgorithm gt menu pid org cishell algorithm convertergraph ConverterGraphaAlgorithm lt menu gt menu type break gt menu name top menu top menu name Preprocessing menu pid edu iu nwb preprocessing extractnodesandedges extractnodes top Extract TopNodesA Igorithm gt menu pid edu iu nwb preprocessing extractnodesandedges extractnodes abovebe low ExtractNodesabovese lo menu pid edu iu nwb preprocessing removeegr aphattributes nodes RemoveNodeAttributesAlgorithmeactory menu pid edu iu iv attacktolerance AttackToleranceAlgorithm gt menu pid edu iu iv errortolerance ErrorToleranceAlgorithm gt menu pid edu iu nwb analysis isolates Delete gt menu type break gt iJi Figure 1 2 Changing the menu structure by editing the default menu xml specification file The file is organized in a hierarchy with menu items nested inside other menu items An item s depth within the hierarchy is signified by how far it is indented and the menu it resides in is determined by what top menu name or menu name it is underneath A menu item takes the form menu pid AlgorithmldentifierHere gt and from within this file the menu items and titles can be customized completely Dividing lines between menu items are represented by menu type break Remember to
30. defining the unit of analysis 3 selection of measures 4 calculation of similarity between units 5 ordination or the assignment of coordinates to each unit and 6 use of the resulting visualization for analysis and interpretation Table 7 1 General steps involved in a scientometric study 1 Data 2 Unit of 3 Layout often one code does both similarity and ordination 6 Display Extraction Analysis Measures steps 4 Similarity 5 Ordination Searches Common Choices Counts Scalar unit by unit matrix Dimensionality Reduction Interaction IST Paper Frequencies Direct linkage Eigenvector Eigenvalue Browse Scopus Journal Attributes Paper citation solutions Pan Google Scholar e g terms Author paper Factor Analysis FA and Zoom Medline Institutional Author Co occurrence Principal Components Filter Author citations Co author Analysis PCA Query Patents Lab Center Co citations Bibliographic coupling Multi dimensional scaling Detail on Grants Dep School By year Co word co term MDS demand Institution Co classification Pathfinder Networks PF Net Broadening Thresholds Co citation Self organizing maps SOM Analysis amp By citation Geolocation By counts Author co citation ACA Topics Model Interpretation By terms County Document co citation DCA State Combined linkage Cluster Analysis Country Scalar Continent Vector unit by attribute Triangulation matrix Force directed placement Topical V
31. e Cancel After clicking the OK button a Directory Tree Prefuse Beta Graph shows up in the Data Manager window Select this dataset and visualize using any of the available tree visualization algorithms see section 4 5 Tree Visualizations for instructions 5 2 Error and Attack Tolerance of Networks Please see http iv slis indiana edu Im Im errorattack html 5 3 Studying Peer to Peer Networks Please see http 1v slis indiana edu Im Im p2p search html 6 Domain Specific Social Science Tutorial originally prepared for Sunbelt 2008 by Ann McCranie Department of Sociology Indiana University Bloomington amccrani indiana edu For this example we will use PSYCHCONSULT an extract from the Staff Study of the Indianapolis Network Mental Health Study This is a file in the Network Workbench nwb format a basic edge list format that can include node and edge attribute information in a text file It is a directed asymmetric unvalued unweighted network that represents the consultation choices among the staff that work in a psychiatric hospital 28 6 1 Load Data Load the sampledata socialscience PS YCHCONSULT nwb dataset after launching the NWB Tool with File gt Load Choose the PSYCHCONSULT data located in the sampledata socialscience folder This folder will be located in your NWB Installation Directory Once you have loaded this network you will see it appear in the right hand Da
32. hit Search button On Sept 21 2008 exactly 50 awards were found Award records can be downloaded in CSV Excel or XML format Save file in CSV format and a sample csv file is available in yournwbdirectory sampledata scientometrics nsf scipolicy csv In the NWB Tool load the file using File gt Load File A table with all records will appear in the Data Manager Right click and view file in Microsoft Office Excel To show how to analyze and visualize funding data we use the active NSF awards data from Indiana University 257 records Cornell University 501 records and University of Michigan Ann Arbor 619 records which were downloaded on 11 07 2008 Save files as csv but rename into nsf Or simply use the files provided in kvournwbdirectory sampledata scientometrics nsf 7 2 3 1Extracting Co PI Networks Load NSF data selecting the loaded dataset in the Data Manager window run Scientometrics gt Extract Co Occurrence Network using parameters 36 MI Extract Network from Table l x Extracts a network from a delimited table Column Name All Investigators v Text Delimiter e O M Y ETE Two derived files will appear in the Data Manager window the co PI network and a merge table In the network nodes represent investigators and edges denote their co PI relationships The merge table can be used to further clean PI names Choose the Extracted Network on Column All Investig
33. indiana edu community n VisualizeData GUESS on more white resizeLinear sitebetweenness 5 red 20 information on how to use the interpreter 26 str nodesbydegree i indegree Visualization GUESS J Io x File Edit Layout Script View Help Acciaiuoli Field Value color 255 24 fixed false height 5 6635 image label Acciaiu labelcolor 0 0 0 aa labelsize 12 7 labelvi true name ni original Acciaiuoli priorates 53 stroke cadetb style 2 totalities 2 visible true wealth 10 width 5 6635 x 112 01 v 70 315 Pazzi kuo ax Ginori Peruzzi U FA Salviati idolfi e Y gt Tornabuoni Acciaiuoli Pucci gt gt gt resizeLinear totalities 5 20 gt gt gt colorize wealth white red gt gt gt l Interpreter A Graph Modifier ri Figure 4 18 Using the GUESS Interpreter 4 6 4 DrL Large Network Layout DrL is a force directed graph layout toolbox for real world large scale graphs up to 2 million nodes Davidson Wylie amp Boyack 2001 Martin Brown amp Boyack in preparation of up to 2 million nodes It includes e Standard force directed layout of graphs using algorithm based on the popular VxOrd routine used in the VxInsight program e Parallel version of force directed layout algorithm e Recursive multilevel version for obtaining better la
34. node is generated with the initial number of edges The probability of attaching to an existing node is calculated by dividing the degree of an existing node by the total number of edges If this probability is greater than zero and greater than the random number obtained from a random number generator then an edge is attached between the two nodes This is repeated in each time step Run with Modeling gt Barab si Albert Scale Free Model and a time step of around 7000 initial number of nodes 2 and number of edges in the input Layout and determine the number and degree of highly connected nodes via Analysis gt Unweighted and Undirected gt Degree Distribution using the default value Plot node degree distribution using Gnuplot 19 4 5 Tree Visualizations Many network datasets come in tree format Examples include family trees organizational charts classification hierarchies and directory structures Mathematically a tree graph is a set of straight line segments edges connected at their ends containing no closed loops cycles A tree graphs 1s also called a simple undirected connected acyclic graph or equivalently a connected forest A tree with n nodes has n graph edges All trees are bipartite graphs Many trees have a root node and are called rooted trees Trees without a root node are called free trees Subsequently we will only consider rooted trees In rooted trees all nodes except the root node have exactl
35. offers options for dealing with these issues Preprocessing gt Remove Self Loops and Preprocessing gt Delete Isolates These options will create a new network file in the Data Manager window You can then select this network and save it with a new name 6 3 Network Analysis NWB implements a few basic algorithms that you can use with an unweighted and directed network like PSYCHCONSULT Make sure your network is highlighted in the Data Manager window In degree and out degree centrality can be calculated with Analysis gt Unweighted and Directed gt Node Indegree Note that this will actually create two files a text file with a sequence of the in degree centrality of the nodes and a new network file that has appended the in degree as an integer attribute Choose this network file apply the NODE OUTDEGREE centrality algorithm and you can create a new network with both measures as attributes Reciprocity can be calculated with Analysis gt Unweighted and Directed gt Dyad Reciprocity This will give you a network level reciprocity measure In this network 17 5 percent of dyads are reciprocal 6 4 Visualization There are several visualization options under development in the NWB Tool To replicate the one shown at this poster presentation you will need to use the GUESS package which is an implementation of the open source program developed by Eytan Adar To learn more about this package visit http graphexplor
36. on how to resize the nodes within GUESS to accomplish something similar with NWB The nodes included can be limited within the Graph Maker according to either their ranking in the sequence of LCS or GCS or their LCS GCS values In NWB this corresponds to Extract Top Nodes or Extract Nodes Above or Below Value To see all nodes set the limit to a number above the number of nodes and click on the Make graph button see HistCite result in Figure 7 23 0 0 i 10 Figure 7 23 Paper citation graph of the FourNetSciResearcher network in HistCite As can be seen this graph includes several isolates 1 e nodes that have no links to or from other nodes in the network 1 e Local Cited References 0 and Local Citation Score 0 According to the textual summary of the dataset given by HistCite there are 59 such isolates in this network These nodes can be marked manually and deleted from the network for a cleaner version of this graph 7 8 3 CiteSpace by Chaomei Chen Compiled by Hanning Guo CiteSpace II is a tool to visualize patterns and trends in scientific literature The Java based tool was developed by Dr Chaomei Chen at Drexel University and can be downloaded from http cluster cis drexel edu cchen CiteSpace For means of comparison we apply CiteSpace to the FourNetSciResearcher dataset containing all of the ISI records of Garfield Wasserman Vespignani and Barabasi Specifically we derive the documen
37. reducing the difficulty of common scientometrics tasks as well as allowing scientometrics functionality to be exposed to non experts Network Workbench embodies both of these trends providing an environment for algorithms from a variety of sources to seamlessly interact in a user friendly interface as well as providing significant visualization functionality through the integrated GUESS tool The reminder of this section compares the Scientometrics functionality in NWB Tool with alternative and complementary tools 7 8 2 HistCite by Eugene Garfield Compiled by Angela Zoss HistCite was developed by Eugene Garfield and his team to identify the key literature in a research field As stated on the Web site HistCite analyzes ISI data retrieved via a keyword based search or cited author search and identifies important papers most prolific and most cited authors and journals other relevant papers keywords that can be used to expand the collection It can also be used to analyze publication productivity and citation rates of individuals institutions countries By analyzing the result of an author search highly cited articles important co author relationships a time line of the authors publications and historiographs showing the key papers and timeline of a research field can be derived A trial version of the tool is available at http www histcite com An interactive version of the FourNetSciResearchers isi analysis result is at http e
38. reported Network Analysis Toolkit NAT was selected Implementer s Timothy Kelley Integrator s Timothy Kelley Reference Robert Sedgewick Algorithms in Java Third Edition Part 5 Graph Algorithms Addison Wesley 2002 ISBN 0 201 31663 3 Section 19 8 pp 205 Documentation https nwb slis indiana edu community n AnalyzeData NetworkAnalysisToolkit This graph claims to be directed Nodes 113 Isolated nodes 0 Node attributes present label area Edges 861 No self loops were discovered No parallel edges were discovered Edge attributes Did not detect any nonnumeric attributes Numeric attributes min max mean weight 1 1 1 This network seems to be valued Average total degree 15 238938053097344 Average in degree 7 619469026548669 Average out degree 7 619469026548671 This graph is weakly connected There are 1 weakly connected components 0 isolates The largest connected component consists of 113 nodes This graph is not strongly connected Mm There are 13 strongly connected components Mm The largest strongly connected component consists of 101 nodes Density disregarding weights 0 06803 Additional Densities by Numeric Attribute densities weighted against standard max weight 0 06803 densities weighted against observed max weight 0 06803 If you should find isolates or self loops in your data they could pose problems for several algorithms that are currently implemented NWB
39. scaleinvanantdynamics Figure 7 27 Network of co author s institutions for FourNetSciResearchers with burst phrases in CiteSpace II 69 7 8 3 5 Comparison Supported Data Formats NWB Tool supports GraphML xml or graphml XGMML xml Pajek NET net Pajek Matrix mat NWB nwb TreeML xml Edge list edge CSV csv ISI 1s1 CiteSpace Il can load ISI export format It also offers converters from SDSS NSF Scopus and Derwent to WOS ISI Medline see Table 7 5 Table 7 5 Network Workbench Tool vs CiteSpace II Function NWB Tool CiteSpace II ISI Scopus y y Google Scholar y y Medline y NSF Citeseer N N Google Scholar y Paper Paper Citation Network Author Paper Consumed Produced y Network Document Co Citation Network y Author Co citation Network Journal Co citation Network Co authorship Network y Network of Co author s institutions Network of Co author s countries Word Co occurrence Network y Subject Categories Co occurrence Network Cited Reference Co Occurrence y Bibliographic Coupling Network L L L L L L L L Cluster View Time Zone View Time slicing Pathfinder Network Detect Duplicate Nodes Merge Duplicate Nodes Betweenness Centrality Extract K core Geospatial Maps E ES Dan L Ee 7 8 3 6 Visualizations CiteSpace II colors nodes and edges by time e g In the co citation network papers by citation year making it ea
40. tables containing all data from the beginning of the time range to the end of the table s time interval which can be seen in the Data Manager and below dl 101 Unique ISI Records SE slice from beginning of 1988 to end of 2007 101 records slice from beginning of 1988 to end of 2002 72 records SE slice from beginning of 1988 to end of 1997 33 records slice from beginning of 1988 to end of 1992 6 records The latter option aligns the output tables according to calendar intervals a El 99 Unique 151 Records pe slice From beginning of 2003 to end of 2007 7 records EFA slice From beginning of 1996 to end of 2002 12 records FEI slice From beginning of 1993 to end of 1997 4 records EEE slice From beginning of 1988 to end of 1992 17 records If user chooses Years under Slice Into time slicing will start from January 1 of the first year If Months is chosen it will start from the first day of the earliest month in the chosen time interval 44 To see the evolution of Vespignani s co authorship network over time check cumulative Then extract co authorship networks for each sliced time table by clicking Scientometrics gt Extract Co Author Network Visualize the evolving network using GUESS as shown in Figure 7 11 Vespignani A My mainin W Pietronero E N Na naa Papito G N fi Vi E iu A i I MN O Vergassola m OE Eman a g IAN o ns LIA
41. to reduce noise and make patterns more salient Smoothing e g averaging using a smoothing window of a certain width and curve approximation might be applied The number of scholarly records over time is plotted to get a first idea of the temporal distribution of a dataset It might be shown in total values or in Yo of total Sometimes it 1s interesting to know how long a scholarly entity was active how old it was in a certain year what growth latency to peak or decay rate it has what correlations with other time series exist or what trends are observable Data models such as the least squares model available in most statistical software packages are applied to best fit a selected function to a data set and to determine if the trend is significant 7 3 1 Charting Trends Please see the Sci Tool at http sci slis indiana edu for geospatial mapping algorithms that are compatible with the NWB Tool 7 3 2 Burst Detection A scholarly dataset can be understood as a discrete time series in other words a sequence of events observations which are ordered in one dimension time Observations here papers exist for regularly spaced intervals e g each month volume or year Kleinberg s burst detection algorithm Kleinberg 2002 identifies sudden increases in the usage frequency of words These words may connect to author names journal names country names references ISI keywords or terms used in title and or
42. without losing context In the NWB Tool select a tree dataset e g generated using the Directory Hierarchy Reader first three levels directories only Run Visualization gt Radial Tree Graph prefuse alpha A window similar to Figure 4 12 will appear displaying the Radial Tree Graph visualization Double click on a node to focus on it and observe the change of the layout Hovering over a node e g the nwb root directory colors it red and all its neighbors blue Like in all other prefuse layouts hold down left mouse button to pan and right button to pan RB Radial Tree Graph prefuse alpha ae edu iu nwb gui brand 1 0 0 f AE ETA INF Mena ger a bundles La org eelorg a lucene 2 3 2 sko org eclipse equinox app F IE dl FreeHEP VectorGraphics 1 0 0 9 re org prefuse lib_20060715 0 0 OSGI INF org eclipse update VE META INF NI GUESS org cishell templates jythonrunner puan NASA NWB Files AO stax_1 0 0 NotUsed Pajek NET Files edu uci ics jung 1 7 4 META INF NotUsed XGMML i org cishell environment equinox feature Pajek MAT Files comanre Ha NE features Guiu nwb javaalgorithm feature ca NotUsed TreeML A edu iu nwb converter feature 1 0 0 GraphML Files ETETOTIKT TOKJO CSV Files sampledata org cishell reference gui feature Configuration Files edu iu nwb gui brand feature 4 0 0 Edge Files org cishell reference feature 1 0 0 biolo A si Ecopus nanol Figure 4 12 Rad
43. 0 y 91 100 File Edit Layout Script View Help JO x 0 Ginori pan arb A Peruzzi Salviati l Pazzi Q D A Medici u N a Tornabuoni Acclaliol Di oh Pucci Object i ly Property labelsize Operator Value y Colour Show Hide Size Show Label Hide Label Change Label Format Edge Labels Node Shape y Center d Change History Resize Linear Colorize Interpreter Graph Modifier m Figure 4 17 Using the GUESS Graph Modifier 4 6 3 2 Interpreter Use Jython a version of Python that runs on the Java Virtual Machine to write code that can be interpreted Here we list some GUESS commands which can be used to modify the layout Color all nodes uniformly g nodes color red g nodes strokecolor red g nodes labelcolor red circle filling circle ring circle label colorize numberofworks gray black for n in g nodes n strokecolor Size code nodes g nodes size 30 n color resizeLinear numberofworks 25 8 Label for i in range 0 50 nodesbynumworks i labelvisible Print for i in range 0 10 print str nodesbydegree i label Edges g edges width 10 g edges color gray make labels of most productive authors visible true Color and resize nodes based on their betweenness colorize wealth The result is shown in Figure 6 Read https nwb slis
44. 0 0102774 Faust K 1992 SOC NET 0 0100571 Anderson CJ 1999 SOC 0 00988407 Albert R 2002 REV MO 0 00958214 Walker ME 1993 SOCIO 0 00957449 Erzan A 1995 REV MOD 0 00865961 Colizza V 2005 PHYSI 0 00791988 Wasserman S 1990 J M Note that the PageRank authority_score and hub_score for each node differ considerably Interestingly Albert R 2002 REV MOD PHYS V74 P47 always appears in the three top 10 nodes sorting list 7 7 3 4 Blondel Community Detection Blondel agglomerates communities hierarchically based on improvements in modularity Blondel Guillaume Lambiotte amp Lefebvre 2008 The algorithm is affected by edge weight but not directionality and it outputs a network whose nodes are annotated with community labels Further documentation can be found in the Sci Tool documentation at http sci slis indiana edu or in the NWB Community Wiki at https nwb slis indiana edu community n AnalyzeData BlondelCommunityDetection 7 7 3 5 Other Community Detection Algorithms The NWB team is currently working on integrating additional community detection algorithms into Network Workbench We hope to include the following algorithms in the near future e Girvan and Newman 1999 which cuts edges in order of descending betweenness centrality to give clusters Girvan amp Newman 2002 e Palla et al 2005 also called CFinder which finds communities based on overlapping cliques and e Reichardt and Bornholdt 2004 which
45. 0 beta Release september 24th 2008 Note save the download as jar Select Your Operating System Windows XP amp Vistal Mn Release Motes Setting Started PDF See more documentation Figure 1 1 Select operating system and download NWB Tool Save the jar file in a new empty yournwbdirectory directory double click the jar file or run the command line java jar yourfilename jar kve Network Uninstall NWB After the successful installation two NWB icons IMi will appear on the Desktop To run NWB Tool double click the Network Workbench icon To uninstall NWB Tool i e to delete all files in yournwbdirectory double click the Uninstall NWB icon WARNING clicking the Uninstall NWB icon will delete every file and folder within yournwbdirectory Make sure all necessary documents are backed up 1 3 Customizing NWB Because NWB is built on CIShell users can fully customize the tool through the use of plugins A thorough walk through of how to develop and integrate plugins can be found at http cishell org n DevGuide NewGuide A user might create or download an NWB plugin or simply receive it via email from a colleague To add the plugin to the tool save the jar file into the yournwbdirectory plugins directory restart the NWB Tool and the plugin will appear in the appropriate menu To delete a plugin exit the NWB Tool and delete the corresponding jar file from
46. 08 Freeman L C 1977 A set of measuring centrality based on betweenness Sociometry 40 35 41 Garfield Eugene 2008 HistCite Bibliometric Analysis and Visualization Software Version 8 5 26 Bala Cynwyd PA HistCite Software LLC http www histcite com accessed on 7 15 08 Gilbert E N 1959 Random Graphs Ann Math Stat 30 1141 Girvan M M E J Newman 2002 Community Structure in Social and Biological networks PNAS 99 7821 7826 Granovetter Mark 1973 The Strength of Weak Ties American Journal of Sociology 78 1360 1380 Griffiths Thomas L Mark Steyvers 2002 A Probabilistic Approach to Semantic Representation Proceedings of the 24th Annual Conference of the Cognitive Science Society Fairfax VA Harzing Anne Wil 2008 Publish or Perish A citation analysis software program http www harzing com resources htm accessed on 4 22 08 Heer Jeffrey Stuart K Card James A Landay 2005 Prefuse A toolkit for interactive information visualization Paper presented at the Conference on Human Factors in Computing Systems Portland OR New York ACM Press pp 421 430 74 Herr II Bruce W Weixia Bonnie Huang Shashikant Penumarthy Katy Borner 2007 Designing Highly Flexible and Usable Cyberinfrastrucutres for Convergence In William S Bainbridge amp Mihail C Roco Eds Progess in Convergence Technologies for Human Wellbeing Vol 1093 pp 161 179 Boston MA Annals of the Ne
47. 1985 Pathfinder Scaling with network structures MCCS 85 Scott J P 2000 Social Network Analysis A Handbook London Sage Publications Shannon P A Markiel O Ozier N S Baliga J T Wang D Ramage N Amin B Schwikowski T Ideker 2002 Cytoscape a software environment for integrates models of biomolecular interaction networks Genome Research 13 11 2498 2504 Siek Jeremy Lie Quan Lee Andrew Lumsdaine 2002 The Boost Graph Library User Guid and Reference Manual New York Addison Wesley Skupin Andr 2000 From Metaphor to Method Cartographic Perspectives on Information Visualization Proceedings of InfoVis 2000 91 97 Small Henry 1973 Co Citation in Scientific Literature A New Measure of the Relationship Between Publications JASIS 24 265 269 Small Henry G E Greenlee 1986 Collagen Research in the 1970 s Scientometrics 10 95 117 Takatsuka M M Gahegan 2002 GeoVISTA Studio A Codeless Visual Programming Environment for Geoscientific Data Analysis and Visualization The Journal of Computers amp Geosciences 28 10 1131 1144 The Thomson Corporation 2008 Reference Manageron 7 15 08 Thelwall M L Vaughan L Bj rneborn 2005 Webometrics In Blaise Cronin Ed Annual Review of Information Science and Technology Vol 39 pp 179 255 Medford NJ Information Today Inc American Society for Information Science and Technology Thomson Reuters 2008a Endnote http
48. 6 509 512 Batagelj Vladimir Ulrik Brandes Efficient Generation of Large Random Networks Physical Review E 71 036113 036118 http www inf uni konstanz de algo publications bb eglrn 05 pdf accessed on 8 31 2009 Batagelj Vladimir Andrej Mrvar 1998 Pajek Program for Large Network Analysis Connections 21 2 47 57 Blondel Vincent D Jean Loup Guillaume Renaud Lambiotte Etienne Lefebvre 2008 Fast unfolding of community hierarches in large networks http arxiv org abs 0803 0476 accessed on 7 17 08 Borgatti S P M G Everett L C Freeman 2002 Ucinet for Windows Software for Social Network Analysis http www analytictech com ucinet ucinet 5 description htm accessed on 7 15 08 Borgman C L J Furner 2002 Scholarly Communication and Bibliometrics In B Cronin amp R Shaw Eds Annual Review of Information Science and Technology Medford NJ Information Today Inc American Society for Information Science and Technology Borner Katy Chaomei Chen Kevin W Boyack 2003 Visualizing Knowledge Domains In Blaise Cronin Ed Annual Review of Information Science amp Technology ARIST Vol 37 pp 179 255 Borner Katy Shashikant Penumarthy Mark Meiss Weimao Ke 2006 Mapping the Diffusion of Information Among Major U S Research Institutions Scientometrics Dedicated issue on the 10th International Conference of the International Society for Scientometrics and Informetrics 68 3 415 426 Bo
49. 70615 jar cCore_3 2 100 7v20070615 jar Core linux_3 2 0 7v20070615 jJar ui_3 2 100 v7v20070615 jar edu iu nwb gui brand_1 0 0 jar edu iu nwb gui brand welcometext_0 0 1 jar 11 Jer org cishell framework 1 0 0 jar org org Cishell reference gui cishell reference gui cishell reference gui org org org org org org org org org org org org org org org org org cishell algorithm convertergraph 1 0 0 jar cishell reference 1 0 0 jar datamanager 1 0 0 jar guibuilder swt 1 0 0 jar Log 1 0 0 Jar cishell reference gui menumanager 1 0 0 jar Cishell reference gui persistence 1 0 0 jar cishell reference gui prefs swt 0 0 1 jar cishell reference gui scheduler 1 0 0 jar cishell reference gui workspace 1 0 0 jar cCishell reference prefs admin 0 0 1 jar cishell reference services 1 0 0 jar cishell cishell Cishell cishell Cishell cishell Cishell a de As 3 3 Converter Plugins edu edu edu edu edu edu edu edu edu edu edu edu edu edu edu edu edu edu edu edu edu edu Tli iu JUs iu TU LU iu tiu atis iu iu EU iu Ls LUG iu iu iu iu iu iu iu nwb nwb nwb nwb nwb nwb nwb nwb nwb nwb nwb nwb nwb nwb nwb nwb nwb nwb nwb nwb nwb HE LEL Ae CESE da LET E bers s3 Ler de Or i L
50. 996 2 Wasserman S 12 1433 1433 1980 1993 3 Galaskiewiez J 11 3 85 3 65 1981 1993 3 80 3 60 1986 1991 3 56 356 1990 1992 7 03 703 1990 1995 7 38 7 38 1992 1997 EAS 3 91 3 91 1995 1998 ondition 1 644 644 1997 2000 Formula Is AND AND G 1 EZ OR G 1 lt F 2 F2 D2 gt 10 3 60 360 1997 1999 ee review of format LO Use 57a Bza was ed 15 Vazquez A 357 3 57 2002 ondition 2 E lai ZN M oe FES oe Formula is ial AND G 1 E2 OR G 1 lt F2 F2 118 Barrat A 6 60 6 60 2004 Preview of format to use Add gt gt Delete Cancel ory O o o c o 8 o o Pietronero L Stanley HE 8 Havlin S 9 Zapperi S 10 Loreto Y 11 Albert R 12 Daruka Jeong H 14 Pastor satorras R x 4 00070 e 1000000 Figure 7 8 Visualizing burst results in MS Excel Running burst detection on the combined Wasserman Vespignani and Barabasi ISI file for authors ISI keywords and cited references results in Figure 7 9 To generate the latter two results select Text Column New ISI Keywords and Cited References instead of Authors in the burst parameters The results reveal many of the trends one would expect to see among these major network science researchers For instance the ISI keywords burst with terms related to diffusion and growth in the early 90s criticality and critical behavior in the late 90s and finish with small world network
51. A Workbench for Network Scientists Network Workbench Tool User Manual 1 0 0 Getting Started General Tutorial Domain Specific Information Science Tutorial Domain Specific Social Science Tutorial Domain Specific Scientometrics Tutorial Updated 09 16 2009 Project Investigators Dr Katy Borner Dr Albert L szl Barabasi Northeastern University Dr Santiago Schnell University of Michigan Dr Alessandro Vespignani Dr Stanley Wasserman and Dr Eric A Wernert Programmers Weixia Bonnie Huang Russell J Duhon Micah W Linnemeier Patrick Phillips Chintan Tank Joseph Biberstine Timothy Kelley Duygu Balcan Mariano Beir Universidad de Buenos Aires Argentina Bruce W Herr II Santo Fortunato Institute for Scientific Interchange Foundation Torino Italy Ben Markines Felix Terkhorn Heng Zhang Megha Ramawat C sar A Hidalgo Harvard University Ramya Sabbineni Vivek Thakre Ann McCranie Alessandro Vespignani and Katy Borner Users Testers amp Tutorial Writers Katy Borner Angela Zoss Hanning Guo Scott Weingart Ann McCranie Mark A Price at Indiana University unless otherwise specified Cyberinfrastructure for Network Science Center School of Library and Information Science Indiana University Bloomington IN http cns slis indiana edu For comments questions or suggestions please post to the nwb helpdesk googlegroups com mailing list Table of Contents Me GEN GS DANCI I o AN Ren aan an
52. Donnell Emilia Martin See MSU Rarles Lake putin O Donnell Emilia M artin peu wf stRaries Lake u a Tang Eran Xloshua Pearce e PA Ge an t Joshua Pearce Rober Minto Dude Luk Robert Minto Dua ik ia NS a apna Deo a estanley asset as estan asset eStephanie Sen o arc 268 Schnell StephaniaSen 1396 Schnell a Waly Borner A 0 Katy Borner Alessandro Vespignani ett Vespignani Albert Laszlo Barabasi Albert Laszlo Barabasi Figure 7 4 Co PI network of Indiana University left and Cornell University right To extract and visualize the largest components e g of Cornell s PI network shown in Figure 7 5 Select network after removing isolates and run Analysis gt Unweighted and Undirected gt Weak Component Clustering with parameter ME Weak Component Clustering x Creates new graphs containing the top connected components Number of top clusters E Cancel Indiana s largest component has 19 nodes Cornell s has 67 nodes Michigan s has 55 nodes Visualize Cornell s network in GUESS using same py script and save via File gt Export Image as jpg 3 Ky Pils Thom 7 Peter Wittich Is aj f Se Gibbons nathan Mi rivalon a TIR Sait AB Johannes Gehrke Ran F John Abovgilao stephen Roehrig 9 lam Welch NN Roger Cappallo ZA g pp sander Weinreb an De gai er Daniel HuttenlocherffAGeoffrey Coate
53. IC SCIENTOME TRICS caia es ja 31 7 EINIRODUCIION sea en a rel 31 PAR AVanable Dataset GA Alo OE UNS EN AA SA VK KA in 31 TA 2G OREA VOLK OW AAA 32 7 2 BIBLIOGRAPHIC DATA ACQUISITION AND PREPARATION oo Women nanas 33 7 2 1 Publication Data IST Scopus and Google Scholar ssssssssssceceneennassseeeeeeennassssseeeneennassssss 34 7 2 2 Personal Bibliographies EndNote and Biota 36 Pcl FUNGO NS DO OE AE UN 36 A2 AS NOLTE LV KA KS KEO un 38 VEMPORADZENA VS IS ag Sa ka AKA a as a a E e 40 Zo I COGAN FERUS naa Kv KK an A Bun 40 SN EKO A Bado VON IERE ETE TETIS TTT AA ES AAA AAA AA AAA mna 40 132 SUCE TADEO INE sees AAA AAA SA l bumo kk TA ENER AAA a Kvo lok OE 44 TA KO Del A IA PN KIER TTT TT TITT TETTE TTT TTT 45 FAI KO 6 ANA GIS ENE TET TT TTT a TTT TETTE TETTE TT naa 46 FA ANIJ OR KN Aly WSIS arses as Secombe Maa hades e TETTE TTETEY TT 46 TAGON DI CCU Bp KU IRE RENE ETE PTT TTT AAA AAA N AAA 47 LOZ COCUT NCE INKUDO i E A una NON nana 50 POCO CUT AN OVON uo OV OE Eu nana KO 55 JT ANALYZING AND VISUALIZING LARGE NEP WORKS o E o a KO ee ai 56 Talak BATEN EVO AS eo E NUK ITT A E EO 56 Ze 2 BEBO LA enU CO OT sou a OE A nana 57 ERO CGOMMUNIV DOPE COM sn o OE KOLEO OA ALKAO 58 TF a LQFOE IN CV OT BOVOVIRO lsh ape Liv ARIS 63 To COMPARISON WITH OTHER POOLS dt 63 TO Genera KON A OJ FEM A AA TII 63 TO a FINI CULE Dy SENO DOEN sss nta SO IVANO AKOR 66 ODIOS Pace Dy CN MET CON us lg A E 67 7 8 4 Pajek by
54. PageRank PR is a link analysis algorithm used by Google to rank web pages Brin amp Page 1998 A page linked to from many others pages with high PageRank can itself achieve a high PR The higher a page s PR the higher it ranks in the Google Search Engine Loet Leydesdorff 2009 compares existing and new indicators for scientometricians including PageRank Ying Ding et al Ding Yan Frazho amp Caverlee 2009 also studied how varied damping factors in the PageRank algorithm influence the author ranking and proposed weighted PageRank algorithms Inc Wikimedia Foundation 2009 The algorithm requires three inputs the network to analyze the number of bins for the distribution and the damping factor which is a probability 1 e a real number between 0 and 1 Default values are provided 61 MA Page Rank PageRank Damping factor The network to analyze must be directed otherwise there are no special constraints In the NWB Tool application of Analysis gt Unweighted and Directed gt PageRank to the FourNetSciResearchers paper citation network adds a PageRank values to each node Figure 7 22 Paper citation network layout of FourNetSciResearchers dataset with nodes size coded according to their PageRank value The top ten nodes with the highest PageRank values are listed below PageRank Paper Oo BEA Pattison P 2000 J MA 0 0128512 Anderson CJ 1992 SOC 62 0 0109013 Colizza V 2006 NAT P 0
55. Pastor satorras R 717084391 Wazquez A 336559477 Oltuai ZM 5 317394305 Barthelemy M 4 37058040 Barrat A 5egissi2d ISI Keywords CIFFUSICR LIMITED AGGREGATION o 387954595 GROWTH 3 72952714 SELF OR SANIZEO CRITICALITY 27413033 CRITICAL BEHAVIGR 4 50199012 ABELIAN SAMDPILE 3154580647 TOPOLOGY 4 02150090 INTERNET 69095400 SMALL WORLD METWOFRES o LiIssi2ys COMPLES NETWORKS 9 66518176 METABOLIT METWORKS 3 73411427 Cited References Holland Pw 1981 JAM STAT ASSOC W76 P33 a siiiz0zi Pietronero L 1955 PHYS REW LETT 461 PS61 10 3073375 Witten TA 1981 PHYS REW LETT W47 P1400 4 288315861 Viesek T 1992 FRACTAL GROWTH PHENO Barabasi AL 1995 FRACTAL CONCEPTS SUR Bak F 1987 PHYS REV LETT 459 P381 Faloutsos M 1999 COMP COMM R 429 P251 Barabasi AL 1999 SCIENCE 2586 P509 1012745764 Ohd224219 3801508017 9 919290203 19 29091604 Watts DI 1990 NATURE 395 P440 11 9306145 Amaral LAN 2000 P NATL ACAD SCI USA 497 P11149 9 0637991956 Albert R 2002 REY MOD PHYS 474 Pa 19 5355355 Newman MEI 2001 PHYS REV E 2 vid Pastorsatorras R 2001 PHYS REV LETT 486 P3200 Pastorsatorras R 2001 PHYS REY LETT Ve Doragovtsey SN 2003 EVOLUTION NETWORKS B 16 46155546 1050409755 01000043643 1115441164 Figure 7 9 Visualization of bursts for author names ISI keywords and cited references It is interesting to note in the cited reference bursts that many of the strongest bursts are the most recent Th
56. RNE VCO View AI TO ZO INOW Bie eerie e ol o 20 49 Sree MOD VIS VAI ZONON AO ico 20 ADD Balloon Graph VISUALIZA NON eseo soko i ke Nan EKKO 21 a IRV MI FOC LV al Ao PO SKAN EE EEE EE TIEI TTE ITE T MI TT EI 22 A30 Radia reel Graph With AAN OAOE A 23 Z OGRAPEIIVISUADIZA TIONS aa an ne eda a ta 23 4 6 1 JUNG based Circular Kamada Kawai Fruchterman Rheingold and Spring LayOUts 23 4 6 2 Prefuse based Specified Radial Tree Graph Force Directed and Fruchterman Rheingold and Small World TRAY QUES sean ANN o SS eee 24 JOI GDESO VISDG ARA OS a ka de ee 24 BO DELTA EN COLONOS NN anna Dl en SO la A Na AN PAN On On AA NA POE RR E E 27 OOO ICA eng TN sss o OO OKE ENT REEN EO No 26 E SAVING VISUALIZATIONS FOR PUBLICATION sagis os la nr en 28 5 DOMAIN SPECIFIC INFORMATION SCIENCE oooooooooooooooooooooooooooooooooooooooooooooooooooo ooooooooooooooooooo oooo 28 o KFAD ANDDISPEAV AvP IE RAR OR sm RENA a ee RK nd A 28 5 2 ERROR AND ATTACK TOLERANCE OF NETWORKS ss sessssccsssscscesssccsssssssqsssseqssssssassesqsasseesassessssssscss 28 5 3 STUDYING PEER TO PEER NETWORKS a Ask ksa ka ka Ka aK EKK DORON VES KOJ LEKADO EKK DRONE DO DV nahan 28 6 DOMAIN SPECIFIC SOCIAL SCIENCE ss LAN hena 28 ON BOA DDATA oe o e KO ON SNOVA 29 02 BASIC NETWORK PROPERTIES ON KA ee 29 OS No ANALY IS aresto o em AR SOME ka o KO ELS eO a no ee Ae So ee eee 30 OZ VISUALIZATION sana BBB Babu A NEO 30 1 DOMAIN SPECIF
57. Title Medline Dandona et al 2001 Financial sustainability Medline Golaz 2007 Chemistry joumals and sustainahiity Medline Dadabhoy and Goltz 2008 Sustainability chemistry is key Medline Rove 2007 Sustainability Education for a sustainable future Medline 2002 Materials for sustainability ai anne Enhancing Uve SAFE strategy through coBaboration Medline Astle et al 2005 o Sceountabihty and suo bility Medline Stanton 2008 Sustainability public health s role in the 2 Ist Carpenter and gt mopparallarn 2000 Lreening up 1 5 getting savvier on ey Medline Done SCHOLARLY DATABASE Cyberinfrastructure for Network Science Center SLIS Indiana University Bloomington Score out of 4 44 4 99 4 38 4 39 3 15 Search Edit Profile About Logout Dawnload fiuo rararda charting atracar fi traen Hee following databace F usero 1976 2007 Foam frisa 2002 Dpering sdb_dornioadzip El F map nee 20041 o E You have horan ba Mollina CAFE 2004 e y seb ekreenkonederin bomo mihia ZIP fro bepress des indikan edu bal ikd Fiere i th i fiet C gmh one E Gaia lila Do this gutometicaly for fles lhe thes From ream cri Figure 7 7 Scholarly Database search results and download interfaces 39 To run burst detection see section 7 3 2 Burst Detection over Medline abstracts simply download matching Medline records Load medline_medline_master csv
58. Vladimir Batagelj et Al 72 7 8 5 Software by Loet Leydesdorff A OR 72 ACKNOWLEDGEMENTS una UA 73 REFERENCES atap Naaah nuansa aa o an 73 SEN Eee an am aa ma 71 1 Getting Started 1 1 Introduction The Network Workbench NWB Tool Herr II Huang Penumarthy amp Borner 2007 is a network analysis modeling and visualization toolkit for physics biomedical and social science research It 1s built on Cyberinfrastructure Shell CIShell Cyberinfrastructure for Network Science Center 2008 an open source software framework for the easy integration and utilization of datasets algorithms tools and computing resources CIShell is based on the OSGi R4 Specification and Equinox implementation OSGi Alliance 2008 The Network Workbench Community Wiki provides a one stop online portal for researchers educators and practitioners interested in the study of networks It is a place for users of the NWB Tool CIShell or any other CIShell based program to get upload and request algorithms and datasets to be used in their tool so that it truly meets their needs and the needs of the scientific community at large Users of the NWB Tool can e Access major network datasets online or load their own networks Perform network analysis with the most effective algorithms available Generate run and validate network models Use different visualizations to interactively explore and understand specific networks Share datasets an
59. a gP gr BK 2 0 arpa k 2 Cria 1 8 k Kiam Figure 4 7 Small world graph left and its node degree distribution equation right Small world properties are usually studied to explore networks with tunable values for the average shortest path between pairs of nodes and a high clustering coefficient Networks with small values for the average shortest path 18 and large values for the clustering coefficient can be used to simulate social networks unlike ER random graphs which have small average shortest path lengths but low clustering coefficients The algorithm requires three inputs the number n of nodes of the network the number k of initial neighbors of each node the initial configuration is a ring of nodes and the probability of rewiring the edges which is a real number between 0 and 1 The network is built following the original prescription of Watts and Strogatz 1 e by starting from a ring of nodes each connected to the k nodes and by rewiring each edge with the specified probability The algorithm run time is O kn Run with Modeling gt Watts Strogatz Small World and input 1000 nodes 10 initial neighbors and a rewiring probability of 0 01 then compute the average shortest path and the clustering coefficient and verify that the former 1s small and the latter is relatively large 4 4 3 Barabasi Albert Scale Free Model The Barabasi Albert BA model is an algorithm which generates a scale free network by i
60. abstract of a paper Rather than using plain frequencies of the occurrences of words the algorithm 40 employs a probabilistic automaton whose states correspond to the frequencies of individual words State transitions correspond to points in time around which the frequency of the word changes significantly The algorithm generates a ranked list of the word bursts in the document stream together with the intervals of time in which they occurred This can serve as a means of identifying topics terms or concepts important to the events being studied that increased in usage were more active for a period of time and then faded away In the NWB Tool the algorithm can be found under Analysis gt Textual gt Burst Detection As the algorithm itself is case sensitive care must be taken if the user desires KOREA and korea and Korea to be identified as the same word As the Garfield ISI data is very different in character from the rest it 1s left out of the burst analysis done here One particular difference is the absence of ISI keywords from most of the works in the Garfield dataset In the NWB Tool use File gt Load and Clean ISI File to load ThreeNetSciResearchers isi which is a file that contains all of Wasserman s Vespignani s and Barabasi s ISI records and is provided as a sample dataset in kyournwbdirectory sampledata scientometrics isi ThreeNetSciResearchers 1s1 The result is two new tables in the Data
61. add a new menu pid AlgorithmldentifierHere gt for custom plugins Save default_menu xml when finished and restart the program to see the changes 1 4 User Interface The general NWB Tool user interface is shown in Figure 1 3 see also Herr II et al 2007 1 Network Workbench Tool File Preprocessing ae Analysis Visualization Scientometrics Help a Console Coke displays data operations otor Data Manager AAA J P LA a A p gt Welcome to the Netwo ar load view etc and reprocessing modeling analysis and visualization of small algorithm ppan parameters Fill selection amp acknowledgements as The Network Workbeni well as error reporting Is supported in part by the NSF Data Manager keeps track of 15 0513650 award The sf Dr Albert Laszl Barab si Dr Santiago all datasets that ore available Schnell Dr Alessandro sse Dr Stanley smo and Dr Eric A Wernert for algorithmic visualization The WWB tool was developed by Webia Huang Russell Duhon Micah Linnemeier Timothy Kelley Duygu or manipulation Balcan Mariano Beir Bruce Herr Santo Fortunato Ben Markines Felix Terkhorn Heng Zhang Megha Ramawat C sar Hidalgo Ramya Sabbineni Vivek Thakres Soma Sanyal Ann MeCranie Alessandro vespionani and Katy Borner It uses the Cyberinfrastructure Shell http cishellorg developed at the Cyberinfrastructure for Network Science Center http cns slis indiana edu at Indiana Univers
62. aper references have no GCC value except for references that are also ISI records Currently the NWB Tool sets the GCC of references to 1 except for references that are not also ISI records This is useful to prune the network to contain only the original ISI records To view the complete network select the network and run Visualization gt GUESS and wait until the network is visible and centered Layout the network e g using the Generalized Expectation Maximization GEM algorithm using GUESS Layout gt GEM Pack the network via GUESS Layout gt Bin Pack To change the background color use GUESS Display gt Background Color To size and color code nodes select the Interpreter tab at the bottom left hand corner of the GUESS window and enter the command lines gt resizeLinear globalcitationcount 1 50 gt colorize globalcitationcount gray black gt for e in g edges e color 127 193 65 255 enter a tab after the three dots hit Enter again Note The Interpreter tab will have gt gt gt as a prompt for these commands It is not necessary to type gt at the beginning of the line You should type each line individually and hit enter to submit the commands to the Interpreter For more information refer to the GUESS tutorial at http nwb slis indiana edu Docs GettingStartedGUESSN WB pdf This way nodes are linearly size and color coded by their GCC and edges are green as show
63. arameterized by a specific set of values r and q n 1 the ones most frequently considered in many different applications This efficiency enables the MST based approach to scale a network with 10 000 nodes and 100 million edges in approximately 129 seconds compared to more than 222 hours using the original approach However the algorithm cannot be applied to directed networks because the sorting process deals with undirected links In the NWB Tool the algorithm can be found under Analysis gt Un Weighted and Undirected gt MST Pathfinder Network Scaling The user has to specify the edge weight column and how the edge weight should be considered Dissimilarity or Similarity 57 MA NSI Pathfinder Network Scaling Ferforms MST based Fathfinder Hetwork Scaling Edge Weight Column number ofcoauthoredworks k Weight to be represented as Dissimilarity Similarity For example when an edge weight represents how many times two authors have co authored that is a measure of similarity and the user should select the Similarity option The provided network should not contain edge weights less than or equal to 0 If it does a warning is generated and a default edge weight is assumed for that edge The output is the pruned network 7 7 2 3 Fast Pathfinder Network Scaling Algorithm The fast pathfinder network scaling algorithm prunes the original network to get an approximation of its PFNET r n 1 in just O n time It u
64. ation cond org index html and be sure to look at the manual and wiki With the original PSY CHCONSULT file you loaded into NWB highlighted it should be the top of the Data Manager choose Visualization gt Guess For this relatively small network the loading time will be a few seconds You will see a second window appear with a network In this new window choose Layout gt Kamada Kawai You may choose to repeat this layout multiple times The basic shape and layout of the network will remain the same but you will notice the orientation changing You can also drag and enlarge the window that the network is in To see specific information about nodes or edges choose View gt Information Window As you pass your mouse over the edges and nodes in the network you will see specific information about these nodes In order to zoom into the network right click on the background and drag to the right Drag left in order to zoom out Dragging with the left mouse button will move the entire network If you lose the network choose View gt Center Currently there are only a few menu based interfaces for the rich options of GUESS For instance you can change the background color via Edit gt Background Color To take advantage of some of the other options of GUESS you can actually type commands into the console located in the bottom of the screen or you can create a script file a simple text file with the extensio
65. ators and run the Analysis gt Network Analysis Toolkit NAT reveals that the number of nodes and edges but also of isolate nodes that can be removed running Preprocessing gt Delete Isolates Select Visualization gt GUESS to visualize Run co PI nw py script Visualizations of co PI network of two universities are given in Figure 7 4 DT eTomEvans a ria Grasa pares ro co At Sch sie i ifi _eLenore Tedesc eTomEvans 8 b Eso Gaela Crowgibwight SchH Emilio Mory Swesh Marru AE GIESE pime FN TT e e o KU Nan hienF ox Meredith Pie arrs Lc AN Shin ka enpor ku orati ER Emilio Morah e e ompte pa guyen ai e ompte N o E EEN guyen eCatherine Pucker On Bech Plat Anderson Nortofr e l kou Bath Plale eAnderson Norton e g gt 2 241 65 955 Stewart Z1 Va Kent OM bras a o hom i uate o e gt NO j ekent OMF kras e Erik Wei Mark Bruhn d gt O s Osii 811 2rk Bruhn O e oavia Bu david mecauray Sej Tho aufmane a BIE gutta z OB vi aufmane e Bogdan Dragnea te pres een Matthews Pa HASTA Pe prsy athows e E 2 e rev ri A a Eget mos Wiliams sdai a d Ooi Ji CT ing ezio Vato PS Sokol p Je Sokol 5 PQ ae e si Cameron e Rp NA Pus ar ina Cano 7 R ee KA cut Edward A EA i wgs ESTA n Sy e gt el i anani Deliyannis dal MV WissinkAta Sarajedini sufik Wiss efis Sarajedin Robert Mathigu Robert Mathieu LA gt ee Niin O
66. d Tools for details The Network Workbench and the Scholarly Database are supported in part by the 21 Century Fund the James S McDonnell Foundation the National Science Foundation under grants IIS 0238261 IIS 0513650 SBE 0738111 the School of Library and Information Science and the Cyberinfrastructure for Network Science Center at Indiana University Any opinions findings and conclusions or recommendations expressed in this material are those of the author s and do not necessarily reflect the views of the National Science Foundation References Adar Eytan 2007 Guess The Graph Exploration System http graphexploration cond org accessed on 4 22 08 Alvarez Hamelin Ignacio Luca Dall Asta Alain Barrat Alessandro Vespignani 2008 LaNet vi http xavier informatics indiana edu lanet vi accessed on 7 17 07 Anthonisse J M 1971 The rush in a directed graph Amsterdam NL Stichting Mathematisch Centrum AT amp T Research Group 2008 Graphviz Graph Visualizaiton Software http www graphviz org Credits php accessed on 7 17 08 Auber David Ed 2003 Tulip A Huge Graph Visualisation Framework Berlin Springer Verlag Barabasi A L 2002 Linked The New Science of Networks Cambridge UK Perseus Barabasi A L R Albert 2002 Statistical mechanics of complex networks Reviews of Modern Physics 74 47 97 Barabasi A L Reka Albert 1999 Emergence of scaling in random networks Science 28
67. d algorithms across scientific boundaries As of August 2009 the NWB Tool provides access to over 100 algorithms and over 60 sample datasets for the study of networks It also allows the loading processing and saving of thirteen file formats NWB GraphML Pajek net Pajek matrix XGMML TreeML ISI Scopus NSF Bibtex Endnote Edgelist and CSV and supports automatic conversion between those formats Additional algorithms and data formats can be integrated into the NWB Tool using wizard driven templates Although the CIShell and the NWB Tool are developed in Java algorithms developed in other programming languages such as FORTRAN C and C can be easily integrated Among others JUNG O Madadhain Fisher amp Nelson 2008 and Prefuse libraries Heer Card amp Landay 2005 have been integrated into the NWB as plug ins NWB also supplies a plug in that invokes the Gnuplot application Williams amp Kelley 2008 for plotting data analysis results and the GUESS tool Adar 2007 for rendering network layouts LaNet vi Alvarez Hamelin Dall Asta Barrat amp Vespignani 2008 for rendering network layouts LaNet vi Alvarez Hamelin et al 2008 uses the k core decomposition to visualize large scale complex networks in two dimensions 1 2 Download and Install The Network Workbench tool is a stand alone desktop application that installs and runs on all common operating systems NWB Tool 0 7 0 and later versions require Java SE 5 versi
68. djacency matrix edge list and visually as structure plot or graph Each non symmetrical occurrence matrix e g paper citations has two associated symmetrical co occurrence matrices e g a bibliographic coupling and a co citation matrix 46 Figure 7 12 shows a sample dataset of five papers A through E published over three years together with their authors x y z references blue references are papers outside this set and citations green ones go to papers outside this set as well as some commonly derived networks The extraction and analysis of these and other scholarly networks is explained subsequently Papers A E written by authors x y 2 over 3 years Paper Paper Citation Network A gt C Each paper happens to have 4 references Papers are connected via direct citation links E Arrows represent information flow from BD Pi 0 1 older papers to younger papers ui Author Author Co Author Network x and y co author papers A and E together Pi Z y and z co author papers A and E A and B are co cited by C and D A and D are co cited by E Reference Co Occurrence Bibliographic Coupling Network references C and D are bibliographically coupled as they 2000 2001 2002 both cite reference A and B B X y Document Co Citation DCA Network n B A Local citation counts within this dataset are given in black and global citation counts ISI times cited are given in green above each paper Figure 7 12 Sample pa
69. e all completed Algorithm Name Date Time Complete MI Load 01 21 2008 03 52 13 PM Figure 4 2 Display of loaded network in the Data Manager window Any file listed in the Data Manager can be saved viewed renamed or discarded by right clicking it and selecting the appropriate menu options If File gt View With was selected the user can select among different application viewers see Figure 4 3 Choosing Microsoft Office Excel for a tabular type file will open MS Excel with the table loaded Please choose an application viewer to read this file View file as AL Doct HTML Document Microsoft Word Document Microsoft Office Excel Cornma Separated Values File Figure 4 3 Select Application Viewer Type for selected file in Data Manager 15 The NWB Tool can save a network using File gt Save which brings up the Save window see Figure 4 4 Note that some data conversions are lossy 1 e not all data is preserved see also sections 2 1 Sample Data and 2 2 Supported Data Formats filetext nwb gt file extxml out_data file extxml XGMML Prefuse in data filetext nwb conversion lossy 4 t Details gt gt Figure 4 4 Select output data type 4 2 Data Conversion Service The NWB Tool can convert between a number of different file types For example Figure 4 4 above shows the different file types in
70. e core architecture and the first seven plugins are IBMs Unstructured Information Management Architecture UIMA http incubator apache org uima the data mining machine learning classification and visualization toolset WEKA http www cs waikato ac nz ml weka Cytoscape Arff2xgmml converter R http www r project org via 1Ggraph and scripts http igraph sourceforge net and yEd Upcoming work will focus on integrating the Cfinder clique percolation analysis and visualization tool http www cfinder org workflow support and web services Several of the tools listed in the table above are also libraries Unfortunately it is often difficult to use multiple libraries or sometimes any outside library even in tools that allow the integration of outside code Network Workbench however was built to integrate code from multiple libraries including multiple versions of the same library For instance two different versions of Prefuse are currently in use and many algorithms use JUNG the Java Universal Network Graph Framework We feel that the ability to adopt new and cutting edge libraries from diverse sources will help create a vibrant ecology of algorithms Although it is hard to discern trends for tools which come from such diverse backgrounds it 1s clear that over time the visualization capabilities of scientometrics tools have become more and more sophisticated Scientometrics tools have also in many cases become more user friendly
71. e parent dataset The Scheduler lets users keep track of the progress of running algorithms 1 5 Workflow Design Many if not all network studies require the application of more than one algorithm A sequence of algorithm calls is also called a workflow A common workflow comprises data loading sampling modeling then preprocessing and analysis and finally visualization Figures 1 4 and 1 5 show the menu items available in the final 1 0 0 release There are domain specific plugin sets e g for scientometrics see Figure 7 1 in section 7 Domain Specific Scientometrics Algorithms that are not applicable to a currently loaded and selected dataset are grayed out Preprocessing Visualization Random Graph GUESS Watks Stragatz Small World Barab si Albert Scale Free Load Extract Top Nodes Load and Clean 151 File Extract Nodes Above or Below Value GnuPlot a all IL Read Directory Hierarchy Remove Node Attributes Datasets Delete High Degree Modes Delete Random Nodes DL WxOrdi Specified prefuse beta Can Chord Hypergrid Circular JUNG PRL Delete Isolates Mier Extract Top Edges Radial Tree Graph F Iph Extract Edges Above or Below Value Sa na Radial Tree Graph with Annotation prefuse beta View with Merge Mode and Edge Files Remove Edge Attributes Split Graph to Mode and Edge Files Remove Self Loops Trim by Degree Tree Map prefuse beta Disc
72. ector space model FDP Term words terms 32 Keyword Latent Semantic Analysis or ontologies words terms including classifications Singular Value Decomp SVD taxonomies etc Correlation if desired Pearson s R on any of above 7 1 2 1 Data Extraction Data are either compiled by hand or downloaded in bulk from major databases For details see section 2 Sample Datasets and Supported Data Formats 7 1 2 2 Units of Analysis Major units of science studied in scientometrics are authors papers and journals as well as other institutional geospatial and topical units Note that a laboratory and or center can be interdisciplinary A department school is typically discipline specific Authors have an address with information on affiliation and geo location Most author consumed produced records have a publication date a publication type e g journal paper book patents grant etc topics e g keywords or classifications assigned by authors and or publishers Because authors and records are associated the geo location s and affiliation s of an author can be linked to the authors papers Similarly the publication date publication type and topic s can be associated with a paper s author s 7 1 2 3 Selection of Measures Statistics such as the number of papers grants co authorships citation over time per author bursts of activity number of citations patents collaborators funding etc or changes of to
73. er Ler JE ter sda Ler Ae ter a ter H ESE He ter de ter Ler conver conver conver conver conver conver conver conver conver conver conver conver conver conver conver conver AS ter a Ler A conver conver converter converter service autostart 1 0 0 jar templates 1 0 0 jar templates jythonrunner 1 0 0 testing convertertester algorithm 0 0 1 jar testing convertertester core new 0 0 1 jar tests ProgressTrackableAlgorithm 1 0 0 jar utilities_1 0 0 jar edgelist 1 0 0 jar jungprefuse 1 0 0O jar jungprefusebeta 1 0 0 jar nwb nwbgraphml 1 0 0 jar nwbpajeknet 1 0 0 jar pajekmat 1 0 0O Jar pajekmatpajeknet_1 0 0 Jar pajeknet_0 7 0 Jar prefusebibtex 0 0 1 jar prefusecsv 0 7 0 jar prefusegraphml 0 7 0 jar prefuseisi 0 7 0 jar prefusensf_0 0 1 jar prefuserefer_0 0 1 jar prefusescopus 0 0 1 jar converter prefuseTreeBetaAlpha 1 0 0 prefusetreeml 1 0 0 jar prefusexgmml 1 0 0 jar tablegraph 1 0 0 jar treegraph 1 0 0 jar 1 0 0 jar jar scipolicy converter psraster_0 0 1 jar 3 4 Algorithm Plugins 3 4 1 Preprocessing edu edu edu edu edu edu edu edu edu edu edu edu edu edu edu edu edu edu 1u iu iu iu iu iu iu iu iu iu iu iu iu L Ui elito iu iu nwb composite extractauthorpapernetwork 0 0 1 jar nwb composite extractcowordfr
74. erent thresholds in Figure 7 19 Higher thresholds result in fewer edges and more network components Figure 7 19 Layout of FourNetSciResearchers dataset with no threshold left with three or more co authorships middle and with 5 or more co authorships right 7 7 3 1 Betweenness Centrality BC refers to the number of times a path from any node in a network to any other node in this network goes through a specific node or edge Freeman 1977 A node or edge that interconnects two sub networks has a high BC value and is also called a gatekeeper or weak link Granovetter 1973 The original algorithm is computationally very expensive and only applicable to networks of up to several hundred nodes In 2001 Ulrik Brandes proposed a more efficient algorithm for betweenness that exploits the extreme sparseness of typical networks Brandes 2001 Other shortest path based indices like closeness or radiality can be computed simultaneously within the same bounds Anthonisse 1971 In the NWB Tool application of Analysis gt Unweighted and Undirected gt Node Betweenness Centrality to the FourNetSciResearchers co authorship network adds BC values to each node The top five nodes with the highest BC value are listed below BC Value Name sis TC Papers 31515 5 Barabasi AL 13496 127 19812 7 Vespignani A 3811 101 3589 5 Garfield E 2469 98 1531 35 Stanley HE 994 22 1528 89 Vazquez A 620 10 Figure 7 20 left shows a
75. ess than or equal to 0 If it does a warning is generated and a default edge weight 1s assumed for that edge The value of R gives the Minkowski distance to use It ranges from to infinity Since R infinity is the most commonly used in Pathfinder network scaling there 1s a checkbox Which when checked indicates that value of R is infinity it is checked by default 7 7 3 Community Detection Diverse algorithms exist to identify sub networks or communities in large scale networks The simplest method is to extract individual connected components which are any maximal set of nodes where every node can be reached from every other node This so called weak component clustering is a useful technique from graph theory because network algorithms generally work independently on each component as no edges exist between components In the NWB Tool running Analysis gt Unweighted and Undirected gt Weak Component Clustering on the FourNetSciResearchers co authorship network results in three unconnected components that can be visualized separately 58 In weighted networks e g co occurrence or co citation networks see section 7 6 2 Co occurrence Linkages thresholds can be applied e g all edges below a certain weight can be omitted leading to a less dense possibly unconnected network As an example we show the application of Preprocessing gt Extract Edges Above or Below Value to the FourNetSciResearchers for diff
76. f nodes in the largest connected component Strong connectedness for directed networks Graph density In the NWB Tool use Analysis gt Network Analysis Toolkit NAT to get basic properties e g for the network of Florentine families available in yournwbdirectory sampledata network florentine nwb The result for this dataset is This graph claims to be undirected Nodes 16 Isolated nodes 1 Node attributes present label wealth totalities priorates Edges 27 No self loops were discovered No parallel edges were discovered Edge attributes Nonnumeric attributes Example value marriag T busines F Did not detect any numeric attributes This network does not seem to be a valued network Average degree 3 375 This graph is not weakly connected There are 2 weakly connected components 1 isolates The largest connected component consists of 15 nodes Did not calculate strong connectedness because this graph was not directed Density disregarding weights 0 225 4 4 Modeling Some research questions require descriptive models to capture the major features of a typically static dataset others demand process models that simulate statistically describe or formally reproduce the statistical and dynamic characteristics of interest B rner Sanyal amp Vespignani 2007 Process models provide insights into why a certain network structure and or dynamics exist The NWB Tool provides different alg
77. fault values Once edges have been removed the network can be visualized by running Visualization gt GUESS In GUESS run the following commands gt for node in g nodes to position the nodes at the DrL calculated place node x node xpos 40 node y node ypos 40 gt resizeLinear references 2 40 gt colorize references 200 200 200 0 0 0 gt resizeLinear weight 1 2 gt q edges color 127 193 637255 and set the background color to white to re create the visualization The result should look something like the one in Figure 7 16 left To visualize the same network using Specified prefuse beta run Visualization gt Specfied prefuse beta and enter xpos for the x parameter and ypos for the y parameter Double right click in the white box to zoom the graph into view The result should look like figure 7 16 right 53 Figure 7 16 Undirected weighted word co occurrence network for FourNetSciResearchers dataset using GUESS left and Specified prefuse beta right 7 6 2 3 Cited Reference Co Occurrence Bibliographic Coupling Network Papers patents or other scholarly records that share common references are said to be coupled bibliographically Kessler 1963 see Figure 7 12 The bibliographic coupling BC strength of two scholarly papers can be calculated by counting the number of times that they reference the same third work in their bibliographies The coupling strength is
78. ference e g in tests of network robustness and epidemic spreading Batagelj amp Brandes In the NWB Tool the random graph generator implements the G n p model by Gilbert Run with Modeling gt Random Graph and input the total number of nodes in the network and their wiring probability The output is a network in which each pair of nodes is connected by an undirected edge with the probability specified in the input A wiring probability of 0 would generate a network without any edges and a wiring probability of 1 with n nodes will generate a network with n 1 edges The wiring probability should be chosen dependent on the number of vertices For a large number of vertices the wiring probability should be smaller 4 4 2 Watts Strogatz Small World A small world network is one whose majority of nodes are not directly connected to one another but still can reach any other node via very few edges It can be used to generate networks of any size The degree distribution is almost Poissonian for any value of the rewiring probability except in the extreme case of rewiring probability zero for which all nodes have equal degree The clustering coefficient 1s high until beta is close to 1 and as beta approaches one the distribution becomes Poissonian This is because the graph becomes increasingly similar to an Erdos Renyi Random Graph see Figure 4 7 Watts amp Strogatz 1998 Inc Wikimedia Foundation 2009 saad o EKD KP y P k m
79. he table of all 361 unique ISI records from the FourNetSciResearchers dataset in the Data Manager window Run Scientometrics gt Extract Co Author Network using the parameter File Format isi The result is two derived files in the Data Manager window the co authorship network and a table with a listing of unigue authors also known as merge table The merge table can be used to manually unify author names e g Albet R and Albert R see example below 50 In order to manually examine and if needed correct the list of unique authors open the merge table e g ina spreadsheet program Sort by author names and identify names that refer to the same person In order to merge two names simply delete the asterisk in the last column of the duplicate node s row In addition copy the uniqueIndex of the name that should be kept and paste it into the cell of the name that should be deleted Table 7 3 shows the result for merging Albet R and Albert R where Albet R will be deleted yet all of the nodes linkages and citation counts will be added to Albert R Table 7 3 Merging of author nodes using the merge table wa EXA ESA e EA Alava M JON O A merge table can be automatically generated by applying the Jaro distance metric Jaro 1989 1995 available in the open source Similarity Measure Library http sourceforge net projects simmetrics to identify potential duplicates I
80. hen the burst weight indicates how much support there is for the burst above the previous bursting level while strength indicates how much support there is for the burst over the non bursting baseline Since the burst detection algorithm was run with bursting state 1 1 e modeled only one burst per word the burst weight is identical to the burst strength in this output To generate a visual depiction of the bursts in MS Excel perform the following steps 1 Sort the data ascending by burst start year 2 Add column headers for all years 1 e enter first start year in G1 here 1980 Continue e g using formulae G 2 until highest burst end year here 2004 in cell AFI 3 In the resulting word by burst year matrix select the upper left blank cell G2 and using Format Conditional Formatting color code the cells according to burst value To color cells for years with a burst total power value of more or equal 10 red and cells with a higher value dark red use the following formulas and format patterns Condition 1 AND AND G 15 E2 OR G 1 F2 F2 D2 gt 10 Condition 2 AND GS1 gt SE2 OR GS1 lt SF2 SF2 Apply the format to all cells in the word by year matrix The result for the given example is shown in Figure 7 8 41 A E FEST wea E E ae Ae E ae ae eee ee AE Word Length Weight Strength Stat End 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1
81. horship network word co occurrence network international collaboration network institute collaboration networks etc The results can be visualized using Pajek Ucinet or NWB The software also includes Acc2 SI exe for the reverse route of turning databases exported from MS Access into the tagged format of the Web of Science IntColl EXE for the analysis and visualization of international collaboration InstColl Exe for the analysis and visualization of institutional collaboration GScholar Exe for the organization of Google Scholar files into files for relational database management MS Access dBase and Google Exe for the organization of Google files into files for relational database management MS Access dBase Simultaneously some of them are available for Chinese Korean and Dutch data 22 Please see the Sci Tool documentation at http sci slis indiana edu for a detailed comparison with Leydesdorff software Acknowledgements Network Workbench investigators are Dr Katy Borner Dr Albert L szl Barabasi Dr Santiago Schnell Dr Alessandro Vespignani Dr Stanley Wasserman and Dr Eric A Wernert The NWB project benefits from input by advisory board members Craig Alan Stewart Noshir Contractor James Hendler Jason Leigh Neo Martinez Michael Macy Ulrik Brandes Mark Gerstein Stephen North and Tom Snijders We acknowledge open source libraries and tools used see sections 3 5 Supporting Libraries and 3 6 Integrate
82. i SprirzEoalticiert should only be used with shape and color options LI l liu Pe Me Acaauol ASO E O TUZ l 3 A Diet muti5pringi ength La Tomin im pm jj j J ae Node Color totakies 0 8 o i l a a Bisher MM Node Shape porte 79 GinoriPgrbadori Mi O 2 Ring Color weth 2 re Albizzi 4 ut EdgeSize business el lo a SN Edge Color manage e oir K se aa Figure 4 15 Force Directed with Annotation prefuse layout 4 6 3 GUESS Visualizations Load the sample dataset yournwbdirectory sampledata networks florentine nwb and calculate an additional node attribute Betweenness Centrality by running Analysis gt Unweighted and Undirected gt Node Betweenness 24 Centrality with default parameters Then select the network and run Visualization gt GUESS to open GUESS with the file loaded It might take some time for the network to load The initial layout will be random Wait until the random layout is completed and the network is centered before proceeding GUESS has three windows called 1 Information window to examine node and edge attributes see Figure 4 16 left 2 Visualization window to view and manipulate network see Figure 4 16 right 3 Interpreter Graph Modifier Window to analyze change network properties below the Visualization window Visualization GUESS 0 x File Edit Layout Script View Help Medici Field Value colo
83. ial Tree Graph visualization of first three levels of nwb directories 22 4 5 5 Radial Tree Graph with Annotation Highlight the same tree dataset and select Visualization gt Radial Tree Graph prefuse beta to set data mapping parameters such as node size node color node shape ring color edge size and edge color A legend will be generated automatically 4 6 Graph Visualizations Most visualization plugins provided in the NWB Tool are designed to layout graphs Examples are the JUNG based Circular layout Kamada Kawai Fruchterman Rheingold and interactive Spring layout prefuse based Specified Radial Tree Graph with or without annotation Force Directed with Annotation and Fruchterman Rheingold with Annotation and Small World layouts GUESS tool supporting more customized visualizations and diverse output formats DrL for visualizing very large networks up to 1 million nodes LaNet 4 6 1 JUNG based Circular Kamada Kawai Fruchterman Rheingold and Spring Layouts Visualizations of the yournwbdirectory sampledata networks florentine nwb network using Circular Kamada Kawai Fruchterman Rheingold and Spring Layouts are given in Figure 4 13 Le Circular JUNG Acciaiuoli Pucci Lamberteschi j Fruch terman Reingold JUNG Figure 4 13 Circular Kamada Kawai Fruchterman Rheingold and Spring JUNG layouts 23 4 6 2 Prefuse based Specified Radial Tree Graph Force Directed
84. iana edu community n VisualizeData DrL 4 6 5 LaNet See https nwb slis indiana edu community n VisualizeData K CoreDecomposition 27 4 6 6 Circular Hierarchy Please see the Sci Tool at http sci slis indiana edu for the Circular Hierarchy algorithm that is compatible with the NWB Tool 4 7 Saving Visualizations for Publication Use File gt Export Image to export the current view or the complete network in diverse file formats such as jpg png raw pdf gif etc 5 Domain Specific Information Science 5 1 Read and Display a File Hierarchy It is interesting to see the structure of file directory hierarchies that can be thought of as a tree with a specific folder at its root and its subfolders and the files within them as its branches In the NWB Tool use File gt Read Directory Hierarchy to parse a file directory hierarchy recursively using parameters Root directory Directory from where to start crawling subfolders and files Levels to recurse Number of levels of depth to recurse hierarchy Recurse the entire tree Check to recurse entire hierarchy Reade directories only Check to disregard files Change the default input by checking Recurse the entire tree and uncheck Read directories only skips files A Read Directory Hierarchy A Root directory C Users User Desktop nwb xp nwb e Levels to recurse 1 e Recurse the entire tree Read directories only skips files
85. ing edge network analysis usable with many programming languages A tool to analyze and visualize scientific literature particularly co citation structures Analysis and visualization tool for data from the Web of Science A statistical computing language with many libraries for sophisticated network analyses A general visualization framework with many capabilities to support network visualization and analysis A tool for visual graph exploration that integrates a scripting environment 64 Command line Graphical Graphical Graphical Graphical Graphical Graphical Graphical Graphical Graphical Command line Graphical Operating System Windows All Major All Major All Major All Major All Major All Major All Major All Major All Major All Major References Leydesdorff 2008 Krebs 2008 Batagelj amp Mrvar 1998 Borgatti Everett amp Freeman 2002 Siek Lee amp Lumsdaine 2002 Brandes amp Wagner 2008 Takatsuka amp Gahegan 2002 Cytoscape Consortium 2008 Auber 2003 Csardi amp Nepusz 2006 Chen 2006 Garfield 2008 Ihaka amp Gentleman 1996 Heer et al 2005 Adar 2007 GraphViz 2004 Networks Visualization Flexible graph visualization Graphical Yes All Major AT amp T software Research Group 2008 Scientom to new algorithms supportive
86. into NWB run Preprocessing gt Normalize Text with a space as New Separator and select abstract Then Run Analysis gt Textual gt Burst Detection with parameters xi Perform Burst Detection on time series textual data amma E LJ General Ratio Boo LJ First Ratio 20 ah Bursting States l 1 Date column published year LJ Date Format VAK LJ Text Column abstract whe Text Separator FO LJ Cancel and space as a text separator The result is a table that shows bursting words together with their length weight strength start and end of burst 7 3 Temporal Analysis Science evolves over time Attribute values of scholarly entities and their diverse aggregations increase and decrease at different rates and respond with different latency rates to internal and external events Temporal analysis aims to identify the nature of phenomena represented by a sequence of observations such as patterns trends seasonality outliers and bursts of activity Data comes as a time series 1 e a Sequence of events observations which are ordered in one dimension time Time series data can be continuous 1 e there is an observation at every instant of time see figure below or discrete 1 e observations exist for regularly or irregularly spaced intervals Temporal aggregations e g over journal volumes years decades are common Temporal analysis frequently involves some form of filtering is applied
87. iographic Data Acquisition and Preparation The NWB Tool reads publication data from Thomson Scientific ISI or Scopus Google Scholar data can be acquired using 3 party tools such as Publish or Perish Harzing 2008 that retrieves papers from Google scholar for a specific data and supports the export into BibTex bib CSV txt or EndNote enw that can be read by NWB Tool Personal references collected via reference management software such as EndNote Thomson Reuters 2008a Reference Manager The Thomson Corporation 2008 or the Bibtex format Feder 2006 can also be read as can funding data downloaded from the National Science Foundation and other scholarly data available in plain comma separated value files Examples are given here 33 7 2 1 Publication Data ISI Scopus and Google Scholar Today most science studies use either Thomson Scientific s databases or Scopus as they each constitute a multi disciplinary objective internally consistent database Google Scholar constitutes a thirds choice Please see comparison of three sources and discussion of coverage in section 7 2 1 Publication Data ISI Scopus and Google Scholar 7 2 1 1 ISI Data For the purposes of this tutorial we exemplarily downloaded publication records from four major network science researchers three of whom are principal investigators of the Network Workbench project Their names ages retrieved from Library of Congress number of citations for highes
88. ion edu iu nwb visualization balloongraph_1 0 0 jar edu iu nwb visualization drl 1 0 0 jar edu tu nawb visualization gnuplot 1 0 0 jar edu iu nwb visualization guess 1 0 0 jar edu iu nwb visualization jungnetworklayout 1 0 0 jar edu iu nwb visualization prefuse alpha smallworld 1 0 0 jar edu iu nwb visualization prefuse beta 1 0 0 jar edu iu nwb visualization radialgraph 1 0 0 jar edu iu nwb visualization roundrussell 1 0 0 jar nwb visualization lanet 1 0 0 jar 3 5 Supporting Libraries antlr stringtemplate 1 0 0 cern colt 1 2 0 comsaibiis1o bel 200 70417 Tar freehep psviewer 0 0 l jar FreeHEP_VectorGraphics_1 0 0 javabib orig 1 0 0 Jar joda_time_1 0 0 jythonlib_2 2 1 lucene_2 3 2 lucene_snowball_1 0 0 stax_1 0 0 uk ac shef wit simmetrics 1 0 0 jar org apache commons collections 3 1 0 org mediavirus parvis 0 4 0 jar edu uci ics jung 1 7 4 edu iu nwb help documentation_0 0 1 jar edu iu nwb shared blondelexecutable_0 0 1 3ar edu iu nwb templates staticexecutable nwb_0 0 1 jarorg mediavirus parvis sampledata_1 0 0 jJar org pretuse lio 20060715 0 0 3 6 Integrated Tools 3 6 1 GUESS GUESS is an exploratory data analysis and visualization tool for graphs and networks The system contains a domain specific embedded language called Gython an extension of Python or more specifically Jython which supports operators and syntax for working on graph structures in an intuitive manner An interactive interpreter bi
89. iption column Table 7 4 Network analysis and visualization tools commonly used in scientometrics research Tools from Loet Leydesdorff for organization analysis and visualization of scholarly data S amp T Dynamics Toolbox In Flow Boost Graph Library GeoVISTA WIN I i I o GUESS 1985 1987 1996 2000 2000 2001 2002 2002 2003 2003 2004 2004 2004 2005 2007 Scientom al Wi SocSci SocSci Statistics Visualiza tion Scientom A V AtV Analysis and Manipulation Visualization Visualization Analysis and Manipulation A V Visualization Visualization Social network analysis software for organizations with support for what if analysis A network analysis and visualization program with many analysis algorithms particularly for social network analysis Social network analysis software particularly useful for exploratory analysis Extremely efficient and flexible C library for extremely large networks Social network analysis tool for research and teaching with a focus on innovative and advanced visual methods GIS software that can be used to lay out networks on geospatial substrates Network visualization and analysis tool focusing on biological networks with particularly nice visualizations Graph visualization software for networks over 1 000 000 elements A library for classic and cutt
90. irected Density disregarding weights 0 02909 Additional Densities by Numeric Attribute densities weighted against standard max numberOfCoAuthoredWorks 0 05123 densities weighted against observed max numberOfCoAuthoredWorks 0 00155 This is especially important if networks are large as the network properties suggest certain data reduction approaches For example if a network is unconnected it might be beneficial to layout components separately identify existing components using Analysis gt Unweighted and Undirected gt Connected Components If a network is very dense then backbone identification and community detection methods discussed below should be applied to identify major structures 7 7 2 Backbone Identification 7 7 2 1 Pathfinder Network Scaling PfNet Pathfinder network scaling is a structural modeling technique originally developed for the analysis of proximity data in psychology Schvaneveldt Durso amp Dearholt 1985 It is assumed to provide a fuller representation of the salient semantic structures than minimal spanning trees but also a more accurate representation of local structures than multidimensional scaling techniques Chen 1999 The algorithm takes a proximity matrix as input and defines a network representation of the 1tems while preserving only the most important links It relies on the so called triangle inequality to eliminate redundant or counter intuitive links Given two li
91. is confirms that many of the foundational techniques of network science have developed recently particularly as the publications with very strong bursts include those by Barabasi and Albert on statistical mechanics of scale free networks Watts and Strogatz on small world networks and Newman on random networks It also reflects a change in dynamics however as the historically social science dominated field of network science now sees major contributions by physicists 7 3 3 Slice Table by Time Use time slicing to see the evolution of a network over time It can be found in Preprocessing gt Slice Table by Time Here we slice the Vespignani dataset into five year intervals from 1988 2007 see Figure 7 10 W Slice Table by Time x Slice a table into groups of rows by time Date Time Column Publication Year Kol Date Tine Format yyy e Slice Into Years e How Many E e From Time 1958 he To Time mr e Align With Calendar LJ Week Starts On Sunday 0 Cancel Figure 7 10 Input values in Slice Table by Time algorithm User should choose Publication Year in the Date Time Column field and leave the Date Time Format field as the default Slice Into allows the user to slice the table by days weeks months quarters years decades and centuries There are two additional options for time slicing cumulative and align with calendar The former produces cumulative
92. ity Please cite as Follows NWB Team 2006 Network Workbench Tool Indiana University and Northeastern University kia L slis edu F Scheduler lists what algorithms you ve used and displays algorithm roqress Remove From List prog l Algorithm Mame i o Complete E Scheduler Figure 1 3 Network Workbench Tool interface components All operations such as loading viewing or saving datasets running various algorithms and algorithm parameters etc are logged sequentially in the Console window as well as in the log files The Console window also displays the acknowledgement information about the original authors of the algorithm the developers the integrators a reference paper and the URL to the reference if available together with an URL to the algorithm description in the NWB community wiki The Data Manager window displays all currently loaded and available datasets The type of a loaded file is indicated by its icon LIE Text text file Table tabular data csv file Matrix data Pajek mat Plot plain text file that can be plotted using Gnuplot Y Tree Tree data TreeML A Network Network data in memory graph network object or network files saved as Graph ML XGMML NWB Pajek net or Edge list format TTT Derived datasets are indented under their parent datasets That is the children datasets are the results of applying certain algorithms to th
93. k Compare the result with Figure 7 13 right and note that this network layout algorithm and most others are non deterministic That is different runs lead to different layouts observe the position of the highlighted node 1n both layouts However all layouts aim to group connected nodes into spatial proximity while avoiding overlaps of unconnected or sparsely connected subnetworks 7 6 1 2 Author Paper Consumed Produced Network There are active and passive units of science Active units e g authors produce and consume passive units e g papers patents datasets software The resulting networks have multiple types of nodes e g authors and papers Directed edges indicate the flow of resources from sources to sinks e g from an author to a written produced paper to the author who reads consumes the paper 7 6 2 Co Occurrence Linkages 7 6 2 1 Author Co Occurrence Co Author Network Having the names of two authors or their institutions countries listed on one paper patent or grant is an empirical manifestation of scholarly collaboration The more often two authors collaborate the higher the weight of their joint co author link Weighted undirected co authorship networks appear to have a high correlation with social networks that are themselves impacted by geospatial proximity Borner Penumarthy Meiss amp Ke 2006 Wellman White amp Nazer 2004 To produce a co authorship network in the NWB Tool select t
94. kipedia 20080 103 science en nwb barabasi bib bibtex bibsonomy bib Epic 20080614 BibTeX bib csv barabasi csv network endnote barabasi enw beetlehorn isi FourNetSciResearchers isi savedrecs barabasi isi savedrecs garfield isi savedrecs vespignani isi savedrecs wasserman isi test5papers isi ThreeNetSciResearchers isi models TARL sampledata Cornell nsf Indiana nsf Michigan nsf SciSIP 07 08 nsf bibtexCoAuthorship properties endnoteCoAuthorship properties isiCoAuthorship properties isiCoCitation properties isiPaperCitation properties mergeBibtexAuthors properties properties mergeEndnoteAuthors properties mergelsiAuthors properties mergelWsfPIs properties mergeScopusAuthors properties nsfCoPI properties nsfPIToProject properties scopusCoAuthorship properties scientometrics nsF scopus BrainCancer scopus florentine nwb friendster graphml xml socialscience Friendster xgmml xml scripts GUESS terror graphml xml terror xgmml xml 1 ma a rete ah Meee Sue Figure 4 9 Tree View visualization with sampledata directory expanded and florentine nwb file highlighted 4 5 2 Tree Map Visualization Tracing its ancestry to Venn diagrams the Treemap algorithm was developed by Ben Shneiderman s group at the HCI Lab at the University of Maryland Johnson amp Schneiderman 1991 It uses a space filling technique to map a tree structure e g file directory into nested rectangles with each rectangle representi
95. l 41 pp 609 641 Medford NJ Information Today Inc 75 Nisonger T E 2004 Citation Autobiography An Investigation of ISI Datbase Coverage in Determining Author Citedness College amp Research Libraries 65 2 152 163 O Madadhain Joshua Danyel Fisher Tom Nelson 2008 Jung Java Universal Network Graph Framework University of California Irvine http jung sourceforge net accessed OSGi Alliance 2008 OSGi Alliance http www osgi org Main HomePage accessed on 7 15 08 Pauly Daniel Konstantinos I Stergiou 2005 Equivalence of Results from two Citation Analyses Thomson ISI s Citation Indx and Google Scholar s Service Ethics in Science and Environmental Politics 2005 33 35 Persson Olle 2008 Bibexcel Umea Sweden Umea University http www umu se inforsk Bibexcel accessed on 7 15 08 Porter M F 1980 An Algorithm for Suffix Stripping Program 14 3 130 137 http tartarus org martin PorterStemmer def txt accessed on 9 23 08 Reichardt Jorg Stefan Bornholdt 2004 Detecting Fuzzy Community Structure in Complex Networks with a Potts Model Physical Review Letters 93 21 218701 Salton Gerard C S Yang 1973 On the Specification of Term Values in Automatic Indexing Journal of Documentation 29 351 372 Schvaneveldt R Ed 1990 Pathfinder Associative Networks Studies in Knowledge Organization Norwood NJ Ablex Publishing Schvaneveldt R W F T Durso D W Dearholt
96. lla slis indiana edu katy outgoing combo Subsequently we compare paper paper citation networks created by NWB Tool and HistCite for the FourNetSciResearchers isi dataset HistCite identifies 360 nodes in this network while NWB identifies 361 unique records The discrepancy is the result of two records that have identical Cite Me As values ANDERSON CJ 1993 J MATH PSYCHOL V37 P299 0 0 NWB is able to distinguish these two records which have unique ISI IDs but are both book reviews by the same reviewer on the same page in the same journal issue HistCite identifies 901 edges between the 360 papers NWB Tool originally identified 5335 nodes and 9595 edges as not only linkages between papers in the set but also linkages to references are extracted The latter nodes can be excluded by removing nodes with a globalCitationCount value of 1 see section 7 6 1 1 Paper Paper Citation Network The resulting network has 341 nodes and 738 edges or 276 nodes and 738 edges after deleting isolates This network can be visualized in HistCite using Tools gt Graph Maker The Graph Maker inputs the nodes of the network which are then laid out chronologically from the top of the screen to the bottom The size of the nodes relates to the value of either the Local Citation Score LCS or the Global Citation Score GCS depending on the 66 type of graph selected The script examples from Scientometrics section 7 6 1 1 give suggestions
97. llaborative and student mentor relationships ties of nationality etc Some regions of scholarship are densely crowded and interactive Others are isolated and nearly vacant Please see the Sci Tool at http sci slis indiana edu for further information on Author Co Citation Networks 7 7 Analyzing and Visualizing Large Networks Most network analysis and visualization algorithms do not scale to millions of nodes Even if a layout is produced it is often hard to interpret Therefore it 1s beneficial to employ algorithms that help identify the backbone 1 e the major connections in a network or identify communities 7 7 1 Basic Network Properties The Analysis gt Network Analysis Toolkit NAT Cisco Systems 2004 can be applied to any size network to compute basic network properties see sample output for FourNetSciResearchers dataset below Nodes 248 Isolated nodes 1 Node attributes present label timesCited numberOfWorks Edges 891 No self loops were discovered No parallel edges were discovered Edge attributes Did not detect any nonnumeric attributes Numeric attributes min max mean number of works 1 33 1 76094 This network seems to be valued Average degree 7 175483870967743 S6 This graph is not weakly connected There are 4 weakly connected components 1 isolates The largest connected component consists of 194 nodes Did not calculate strong connectedness because this graph was not d
98. lso provide spectral clustering and expectation maximization clustering see Figure 7 30 71 Figure 7 30 Spectral clustering left and expectation right maximization clustering of document co citation network of FourNetSciResearchers Network Extraction A comparison of networks that are extracted by NWB Tool and CiteSpace II can be found in Table 7 5 As for word co occurrence networks NWB Tool can process the words from any data field but only from one field at a time CiteSpace II can process words occurring in title abstract descriptors and or identifiers fields Burst Detection Both tools support burst detection for time stamped author names journal names country names references ISI keywords or terms used in title and or abstract of a paper NWB can detect different types of burst according to the need of research CiteSpace II can detect burst phrases form noun phrases or plain text and visualize them It notes that Noun Phrases are identified using part of speech tagging Plain text terms are identified by sliding window term selector 7 8 4 Pajek by Vladimir Batagelj et al Please see the Sci Tool documentation at http sci slis indiana edu for a comparison with Pajek http pajek imfm si doku php covering supporting network analysis and visualization 7 8 5 Software by Loet Leydesdorff Leydesdorff s software is a suite of programs with different analysis functions It contains the analysis of co aut
99. models community structure as identical equilibrium spin states Reichardt amp Bornholdt 2004 7 7 4 Large Network Layout The NWB Tool supports the layout of networks with up to 10 million nodes via DrL formerly called VxOrd Davidson et al 2001 For example it is possible to select a co citation network and run Visualization gt DrL VxOrd with parameters Edge Cutting 0 8 Edge Weight Attribute weight X Position Attribute xpos Y Position Attribute ypos The result is a Laid Out Network file that contains x y positions for all nodes The file can be visualized using GUESS or Visualization gt Specified prefuse beta with parameters x xpos y Yypos Note that only node positions are generated Color and size coding of nodes and edges has to be done in a separate step For a step by step tutorial using DrL see section 7 6 2 2 Word Co Occurrence Network 7 8 Comparison with Other Tools 7 8 1 General Comparison Table 7 4 provides an overview of existing tools used in scientometrics research see also Fekete amp B rner chairs 2004 The tools are sorted by the date of their creation Domain refers to the field in which they were originally 63 developed such as social science SocSci scientometrics Scientom biology Bio geography Geo and computer science CS Coverage aims to capture the general functionality and types of algorithms available e g Analysis and Visualization A V see also descr
100. n py Either type the following commands into the console or create a py file in a text editor To run the script file chose File gt Run Script and choose your newly created script file g nodes labelvisible true 30 for node in g nodes node x node x 10 node y node y 10 colorize label label psychiatrist color red label med dir inpt unit color red label nurse color blue clusts groupBy area for c in clusts createConvexHull c randomColor 50 After you have entered these commands or run this script file your network might disappear off screen Choose View gt Center to refocus on the network These commands make the labels visible change the sizes of the nodes and edges colors the labels the same color being assigned to nodes that have the same label of job type thus psychiatrists and the medical director red while nurses are colored blue The script also creates hulls or demarcations of areas of the network based on the attribute of area You can experiment with the random colors assigned to the hulls by rerunning this script or line To save your visualization choose File gt Export Image 7 Domain Specific Scientometrics 7 1 Introduction 7 1 1 Available Datasets and Algorithms Scientometrics specific sample datasets can be found in nwb sampledata scientometrics bibtex personal bibliography files endnote personal bibliography files isi
101. n Sociology New Y ork Routledge Leydesdorff Loet 2008 Software and Data of Loet Leydesdorff http users fmg uva nl lleydesdorff software htm accessed on 7 15 2008 Leydesdorff Loet 2009 How are new citation based journal indicators adding to the bibliometric toolbox Journal of the Amercian Society for Information Science amp Technology 60 7 1327 1336 http users fmg uva nl lleydesdorff journal_indicators accessed on 8 31 2009 Marshakova I V 1973 Co Citation in Scientific Literature A New Measure of the Relationship Between Publications Scientific and Technical Information Serial of VINITI 6 3 8 Martin S W M Brown K W Boyack in preparation DrL Distributed Recursive Graph Layout Journal of Graph Algorithms and Applications Meho Lokman I Kiduk Yang 2007 Impact of Data sources on Citation Counts and Rankings of LIS Faculty Web of Science versus Scopus and Google Scholar Journal of the American Society for Information Science and Technology 58 13 2105 2125 http www3 interscience wiley com cgl bin fulltext 116311060 PDFSTART accessed on 9 23 08 Monge P R N Contractor 2003 Theories of Communication Networks New York Oxford University Press Narin F J K Moll 1977 Bibliometrics Annual Review of Information Science and Technology 12 35 38 Nicolaisen Jeppe 2007 Citation Analysis In Blaise Cronin Ed Annual Review of Information Science and Technology Vo
102. n in Figure 7 13 left Any field within the network can be substituted to code the nodes To view the available fields open the Information Window Display gt Information Window and mouse over a node Also note that each ISI paper record in the network has a dandelion shaped set of references The GUESS interface supports pan and zoom node selection and details on demand see GUESS tutorial For example the node that connects the Barabasi Vespignani network in the upper left to Garfield s network in the lower left is Price 1986 Little Science Big Science The network on the right is centered on Wasserman s works 48 Guess Yisualization loj x Guess Visualization ik ID x File Edit Display Layout Help File Edit Display Layout Help Barabasi AL 1999 S 5 Field value color 0 0 0 255 eV fixed false 7 El globaltimescited 2218 5 height 50 0 image lndegree 2 inoriginaldataset true label Barabasi AL 1 labelcolor labelvisible false localtimescited 55 h name n237 IN Price DJ 1986 LITTLE SCI BIG pr E 7 strokecolor cadetblue A Y style 2 Y 7 totaldegree 56 1 UE visible true TAM i width 50 0 e 1 x 160 94166564 gt FesizeLinear gropalcimescicea Z 40 F y 95 74182128 gt gt gt colorize globaltimescited 200 200 200 255 black KI gt gt gt g computeDegrees LV 7 gt gt gt colorize indeg
103. n the NWB Tool simply select the co author network and run Scientometrics gt Detect Duplicate Nodes using the parameters Attribute to compare on label Merge when this similar 0 95 Create notice when this similar 0 85 Number of shared first letter 2 The result is a merge table that has the very same format as Table 7 3 together with two textual log files The log files describe which nodes will be merged or not merged in a more human readable form Specifically the first log file provides information on which nodes will be merged right click and select view to examine the file while the second log file lists nodes which will not be merged but are similar Based on this information the automatically generated merge table can be further modified as needed In sum unification of author names can be done manually or automatically independently or in conjunction It is recommended to create the initial merge table automatically and to fine tune it as needed Note that the same procedure can be used to identify duplicate references simply select a paper citation network and run Scientometrics gt Detect Duplicate Nodes using the same parameters as above and a merge table for references will be created To merge identified duplicate nodes select the merge table and the co authorship network holding down the Ctrl key Run Scientometrics gt Update Network by Merging Nodes This will produce an updated network as
104. na 4 K3 UTI TR OPC ION ara LE oO ada 4 2 DOWNLOADAND NS TA BE idad 4 Ko CUSTOMIZING NW Bastidas 6 TAP SE RUIN UE sn datas eo bo 7 Ld WW ORKELOW DESIGN sasa T AAT A 8 2 SAMPLE DATASETS AND SUPPORTED DATA FORMATS sssccessccesssccsssccssscccsscccsssccssscccessccssscccssss 9 da AMPLE DA TAS BS Aa ka ONDE EKO RO E NO EEE ON ON ea aa EREN SOJ 9 PALA DIAN NN ON RIMA ES sapeo e kos da ld an SEN Ro 10 3 CODE LIBRARY EEE TT TTEETTITTTTT TIE cita 11 NIO GIM PECI TTT TTT TTE TETTE TTT a 1 JA Aa Na On TTTE TETTE TT an PA a a TTT 11 CONVERTER PN A KO o KO OOK A NO UN 12 SS eAEGORI THI BEBGINS sas ask as Do a ea e koda 12 SIU ipo KU CSS TAE EEE I ITE TETTE TETTE non ETT ERE 12 SAE VIA EET ETIO aU BN 13 se Seu MO CUE AA AAA AAA AAA AA AAA ANA AAA TETTE III 15 SA SAS A RE 14 525 SD UPPOR RING LIBRARIES ass 14 OJINIBGRATEDSI OF sima TO o ea ea TREO AO ST lng KO OO AN AN eee 14 A A te Pee PER TT 14 OZ TIO AKO eda ees seca ITU NN ENI ANEN KREOJ 14 4 GENERAL TUTORIAL sss oz udono d on rGo AB bo seksosaka 14 olu OND IF XX 3 31ND S AVE IT uan ene uran su E o ea 14 4 2 DATA CONVERSI N SERVICE sss Sn AS A A A ED So tad NUN 16 A 3 COMPUTE BASIC NEFWORK STATISTICS babe as idas 16 AZENMODE BINO e presa a o epo ano kuo l aa e La ti 17 DAR Rane POD GE MORE rn ro A BD BELU oath aa Me etal at ae 17 Wat SIV OAL SIAL ON ras da SNI o ua BR IS 44 5 Barabusi Albert Scale Free Model ornica a abo sides 19 AS REE VISUADIZA ON Brass A KARESAN 20
105. ncorporating growth and preferential attachment Starting with an initial network of a few nodes a new node 1s added at each time step Older nodes with a higher degree have a higher probability of attracting edges from new nodes The probability of attachment is given by Pada The initial number of nodes in the network must be greater than two and each of these nodes must have at least one connection The final structure of the network does not depend on the initial number of nodes in the network The degree distribution of the generated network is a power law with a scaling coefficient of 3 Barabasi amp Albert 1999 Barabasi amp Albert 2002 Figure 4 8 shows the network on the left and the probability distribution on a log log scale on the right i 0 01 1e04 CL 12 06 12 08 i 10 100 1000 Figure 4 8 Scale free graph left and its node degree distribution right This is the simplest known algorithm to generate a scale free network It can be applied to model undirected networks such as the collaboration network among scientists the movie actor network and other social networks where the connections between the nodes are undirected However it cannot be used to generate a directed network The inputs for the algorithm are the number of time steps the number of initial nodes and the number initial edges for a new node The algorithm starts with the initial number of nodes that are fully connected At each time step a new
106. nds the text that you type in the interpreter to the objects being visualized for more useful integration GUESS also offers a visualization front end that supports the export of static images and dynamic movies For more information see https nwb slis indiana edu community n VisualizeData GUESS 3 6 2 Gnuplot Gnuplot is a portable command line driven interactive data and function plotting utility for UNIX IBM OS 2 MS Windows DOS Macintosh VMS Atari and many other platforms For more information see http www gnuplot info 4 General Tutorial 4 1 Load View and Save Data In the NWB Tool use File gt Load File to load one of the provided in sample datasets in yournwbdirectory sampledata or any dataset of your own choosing see section 2 Sample Datasets and Supported Data Formats and Figure 4 1 14 Select a File Look in 2 network la bio TARL My Recent IE netsci06 conference net Documents florentine nwb conversionGraph graphml xml Copy of Friendster graphml xml friendster graphml xml E Friendster xgmml xml seiyu graphml xml 2 terror graphml xml terror xgmml xml File name florentine nwb My Network Files of type ze Figure 4 1 Select a file The result will be listed in the Data Manager window see Figure 4 2 Network Workbench Tool File Preprocessing Modeling Analysis Search Visualization Tools Con
107. neralization 1 e the simplification enlargement displacement merging or selection of entities that does not affect their symbology and 2 conceptual symbolization merging selection plus symbolization and enhancement of entities e g representing high density areas by a new city symbol Kraak amp Ormeling 1987 45 Geometric Generalization aims to solve the conflict between the number of visualized features the size of symbols and the size of the display surface Cartographers dealt with this conflict mostly intuitively until researcher s like Friedrich Topfer attempted to find quantifiable expressions for it Skupin 2000 Tobler 1973 Topfer 1974 Topfer amp Pillewizer 1966 Please see the Sci Tool at http sci slis indiana edu for geospatial mapping algorithms that are compatible with the NWB Tool 7 5 Topical Analysis The topic also called semantic coverage of a unit of science can be derived from text associated with it For example topic coverage and topical similarity of e g authors or institutions can be derived from units associated with them e g papers patents or grants Topical aggregations e g over journal volumes scientific disciplines or institutions are common Topic analysis extracts the set of unique words or word profiles and their frequency from a text corpus Stop words such as the of etc are removed Stemming 1 e the reduction of words such as scientific
108. ng a node 20 A rectangular area is first allocated to hold the representation of the tree and this area is then subdivided into a set of rectangles that represent the top level of the tree This process continues recursively on the resulting rectangles to represent each lower level of the tree with each level alternating between vertical and horizontal subdivisions The parent child relationship 1s indicated by enclosing the child rectangle by its parent rectangle That is all descendents of a node are displayed as rectangles inside its rectangle Associated with each node is a numeric value e g size of a directory and the size of a node s rectangle is proportional to its value Shneiderman s Treemaps for space constrained visualization of hierarchies webpage http www cs umd edu hcil treemaps provides the full Story In the NWB Tool select a tree dataset e g generated using the Directory Hierarchy Reader in the Data Manager window then run Visualization gt Tree Map prefuse beta A window similar to Figure 4 10 will appear displaying the Tree Map visualization Use the search box in the bottom right corner to enter a search terms and matching files will highlight pink The darker the box the deeper the file in the hierarchy The box dimensions are a relative measure of file size configuration plugins converter test files sampledata workspace scriptswb ex nvdalinseprc Figure 4 10 Tree Ma
109. nks or paths in a network that connect two nodes the link path is preserved that has a greater similarity defined via the Minkowski metric It is assumed that the link path with the greater similarity better captures the interrelationship between the two nodes and that the alternative link path with lower similarity is redundant and should be pruned from the network Two parameters r and g influence the topology of a pathfinder network The r parameter influences the weight of a path based on the Minkowski metric The g parameter defines the number of links in alternative paths length of a path up to which the triangle inequality must be maintained A network of N nodes can have a maximum path length of g N 1 With g N the triangle inequality is maintained throughout the entire network For details on the method and its applications see Schvaneveldt 1990 The NWB Tool provides two more scalable versions of pathfinder network scaling as discussed below 7 7 2 2 MST Pathfinder Network Scaling The original pathfinder network scaling algorithm has a runtime of O n which limits its applicability to larger networks One alternative is the MST Pathfinder algorithm which prunes the original network to get its PFNET 92n 1 in just O n log n time The underlying idea comes from the fact that the union superposition of all the Minimum Spanning Trees extracted from a given network is equivalent to the PFNET resulting from the Pathfinder algorithm p
110. normalization utilizes the StandardAnalyzer provided by Lucene http lucene apache org It separates text into word tokens normalizes word tokens to lower case removes s from the end of words removes dots from acronyms and deletes stop words Soon the Porter stemmer http tartarus org martin PorterStemmer will become available as well The result is a derived table in which the text in the abstract column is normalized Select this table and run Scientometrics gt Extract Word Co Occurrence Network using parameters Node Identifier Column Cite Me As Text Source Column Abstract Text Delimiter Aggregate Function File None The outcome is a network in which nodes represent words and edges denote their joint appearance in a paper Word co occurrence networks are rather large and dense Running the Analysis gt Network Analysis Toolkit NAT reveals that the network has 2 888 word nodes and 366 009 co occurrence edges There are 235 isolate nodes that can be removed running Preprocessing gt Delete Isolates Note that when isolates are removed papers without abstracts are removed along with the keywords The result is one giant component with 2 653 nodes and 366 009 edges To visualize this rather large network run Visualization gt DrL VxOrd with default values To keep only the strongest edges run Preprocessing gt Extract Top Edges using parameters Top Edges 1000 and leave the others at their de
111. ome available in the Data Manager It has 5 335 nodes 213 of which are isolates and 193 039 edges Isolates can be removed running Preprocessing gt Delete Isolates The resulting network has 5122 nodes and 193 039 edges and is too dense for display in GUESS Edges with low weights can be eliminated by running Preprocessing gt Extract Edges Above or Below Value with parameter values Extract from this number 4 Below leave unchecked Numeric Attribute weight Here only edges with a local co citation count of five or higher are kept The giant component in the resulting network has 265 nodes and 1 607 edges All other components have only one or two nodes The giant component can be visualized in GUESS see Figure 7 18 right see the above explanation and use the same size and color coding and labeling as the bibliographic coupling network Simply run GUESS File gt Run Script and select yournwbdirectory sampledata isi reference co occurence nw py 55 Figure 7 18 Undirected weighted bibliographic coupling network left and undirected weighted co citation network right of FourNetSciResearchers dataset with isolate nodes removed 7 6 3 2 Author Co Citation Network ACA Authors of works that are repeatedly juxtaposed in references cited lists are assumed to be related Clusters in ACA networks often reveal shared schools of thought or methodological approach common subjects of study co
112. omtable 1 0 0 jar nwb composite extractpapercitationnetwork 0 0 1 jar nwb composite isiloadandclean 0 0 1 jar nwb preprocessing bibcouplingsimilarity 0 9 0 jar nwb preprocessing cocitationsimilarity 1 0 0 jar nwb preprocessing csv 1 0 0 jar nwb preprocessing duplicatenodedetector 1 0 0 jar nwb preprocessing extractnodesandedges 0 0 1 jar nwb preprocessing pathfindernetworkscaling fast 1 0 0 jar nwb preprocessing pathfindernetworkscaling mst 1 0 0 jar nwb preprocessing prefuse beta directoryhierarchyreader 1 0 0 jar nwb preprocessing removegraphattributes 0 0 1 jar nwb preprocessing tablefilter 1 0 0 jar nwb preprocessing text normalization 1 0 0 jar nwb preprocessing timeslice 1 0 0 jar nwb preprocessing trimedges 1 0 0 jar nwb tools mergenodes 1 0 0 jar 12 edu iu nwb util 1 0 0 jar edu iu nwb shared isiutil_1 0 0 jar 3 4 2 Analysis edu edu edu edu edu edu edu edu edu edu edu edu edu edu edu edu edu edu edu edu edu edu edu edu edu edu edu edu edu edu edu edu edu edu edu edu edu edu edu edu edu edu edu edu edu edu iu iu iu Sebis iu iu iu iu iu iu iu iu iu iu iu iu iu iu FU iu iu iu iu is iu iu iu Jels istis LU iu o ls iu Tus iu iu LUG eti Its iu iu iu Tiz iu iu nwb nwb nwb nwb nwb nwb nwb nwb nwb nwb
113. on 1 5 0 or later to be pre installed on your local machine You can check the version of your Java installation by running the command line java version If not already installed on your computer download and install Java SE 5 or 6 from http www java com en download index jsp To download NWB Tool go to http nwb slis indiana edu download html and select your operating system from the pull down menu see Figure 1 1 Network Workbench Download Mozilla Firefox O x File Edit wiew History Bookmarks Tools Help e Sa Ga E x A http mb slis indiana edufdownload htrmi LY li Gl Googe P Most Visited dife Getting Started Latest Headlines Ik Customize Links C AN A Workb h tor Network Scientists LOGIN Home People Research Publications Community Documentation DevZone About Download HWE Tool 1 0 0 beta 5 development release Downloads for NWB Tool Releases March 23rd 2009 37 000 This release contains the most up to date features but has not been fully tested save the download as jar Select Your Operating System 25 000 f DOWNLOAD 10 000 HWE Tool 1 0 0 beta 4 development release November 13th 2006 This release contains the most up to date features but has not been fully tested 15 000 save the download as jar Select Your Operating System Number of Downloads per Month 5 3 a Windows XP amp Vista DOWNLOAD q 00 Release Motes HWB Tool 1 0
114. orithms to model networks such as Random Graph Small World and Barab si Albert Scale Free models discussed below as well as diverse peer to peer modeling algorithms http v slis indiana edu lm lm p2p search html and a Discrete Network Dynamics tool https nwb slis indiana edu community n ModelData DiscreteNetworkDynamics 4 4 1 Random Graph Model The random graph model generates a graph that has a fixed number of nodes which are connected randomly by undirected edges see Figure 4 6 left The number of edges depends on a specified probability The edge probability is chosen based on the number of nodes in the graph The model most commonly used for this purpose was introduced by Gilbert 1959 This is known as the G n p model with n being the number of vertices and p the linking probability The number of edges created according to this model is not known in advance Erd s R ny1 introduced a similar model where all the graphs with m edges are equally probable and m varies between 0 and n n 1 2 Erd s amp R nyi 1959 This is known as the G n m model The degree distribution for this network is Poissonian see Figure 4 6 right 17 0 20 0 15 PUK 0 0 05 0 00 Figure 4 6 Random graph and its Poissonian node degree distribution Very few real world networks are random However random networks are a theoretical construct that is well understood and their properties can be exactly solved They are commonly used as a re
115. oth terms Abstract Double guotation can be used to match compound All Text sustainability terms e g breast cancer retrieves records with the phrase breast cancer and not records where Search First Year 1898 y breast and cancer are both present but not the j exact phrase Last Year 2008 The importance of a particular term in a query can be increased by putting a and a number after the O I term For instance breast cancer 10 would increase Medline 1898 2008 the importance of matching the term cancer by ten T iH 1961 2002 compared to matching the term breast I use 1985 2004 I uspto 1976 2007 Figure 7 6 Scholarly Database Home page and Search page Results are displayed in sets of 20 records ordered by a Solr internal matching score The first column represents the record source the second the creators third comes the year then title and finally the matching score Datasets can be downloaded as dump for future analysis scholarly Database Results Mozilla Firefox Schaller hab aba Dermarikul Harilla Finch A Most votes MP Getting Started i Latest Meadines Search Edit Profle About Logout Your tearch returned 2 790 resulte in 0 458 seconds OV Download Total results per database HIM 70 Medine 2 400 USPTO 16 NGF 296 Results 1 through 20 Next gt gt Sourne Authors Creaturs Year
116. p visualization of complete nwb directory hierarchy 4 5 3 Balloon Graph Visualization A balloon graph places the focus node in the middle of the canvas and all its children in a circle around it Children of children are again places in a circle around their parents etc In the NWB Tool select a tree dataset e g generated using the Directory Hierarchy Reader and run Visualization gt Balloon Graph prefuse alpha A window similar to Figure 4 11 will appear displaying the Balloon Graph visualization Double click on a node to focus on it and observe the change of the layout Like in all other prefuse layouts hold down left mouse button to pan and right button to pan 21 Isi BalloonTree ors Age O 2 la i UN uu vuo rkspace Wy logs iU MT sampledata luginz TET LJ gle A Figure 4 11 Balloon Tree visualization of complete nwb directory hierarchy 4 5 4 Radial Tree Visualization Radial trees layout uses a focus context fisheye technique for visualizing and manipulating very large trees The focused node 1s placed in the center of the display and all other nodes are rendered on appropriate circular levels around that selected node The further away a node is from the center the smaller it is rendered This way potentially very large rooted or unrooted trees such controlled vocabularies taxonomies or classification hierarchies can be displayed Users can focus on particular parts of those trees
117. papers The enormous differences in citation dynamics between physics and social sciences such as scientometrics or sociology are visible Vespignani and Barabasi are both physicists and their papers very rapidly acquire citation counts Note that neither books nor Conference papers are captured in this dataset 34 7 2 1 2 Scopus Data The NWB Tool reads publication data from Scopus see https nwb slis indiana edu community n LoadData Scopus 7 2 1 3 Google Scholar Google Scholar data can be acquired using Publish or Perish Harzing 2008 that can be freely downloaded from http www harzing com pop htm A query for papers by Albert L szl Barabasi run on Sept 21 2008 results in 111 papers that have been cited 14 343 times see Figure 7 2 Harzing s Publish or Perish i P ioj File Edit View Help Author impact analysis Perform a citation analysis for one or more authors Author impact analysis Journal impact analysis Query General citation search EEE Albert L szl Barab si IV Biology Life Sciences Environmental Science Multi query center TTT TEJ yq IV Business Administration Finance Economics IV Chemistry and Materials Science Lookup Direct Year of publication between O and 0 Iv Engineering Computer Science Mathematics Help Check for updates IV Medicine Pharmacology Veterinary Science IV Physics Astronomy Planetary Science IV Social Sciences Arts Humanities About Publish or Perish
118. per network left and four different network types derived from it right Diverse algorithms exist to calculate specific node edge and network properties see B rner et al 2007 Node properties comprise degree centrality betweenness centrality or hub and authority scores Edge properties include are durability reciprocity intensity weak or strong density how many potential edges in a network actually exist reachability how many steps it takes to go from one end of a network to the other centrality whether a network has a center point s quality reliability or certainty and strength Network properties refer to the number of nodes and edges network density average path length clustering coefficient and distributions from which general properties such as small world scale free or hierarchical can be derived Identifying major communities Via community detection algorithms and calculating the backbone of a network via pathfinder network scaling or maximum flow algorithms helps to communicate and make sense of large scale networks 7 6 1 Direct Linkages 7 6 1 1 Paper Paper Citation Network Papers cite other papers via references forming an unweighted directed paper citation graph It is beneficial to indicate the direction of information flow in order of publication via arrows References enable a search of the citation graph backwards in time Citations to a paper support the forward traversal of the gra
119. ph Citing and being cited can be seen as roles a paper possesses Nicolaisen 2007 In the NWB Tool load the file yournwbdirectory sampledata scientometrics isi FourNetSciResearchers isi using File gt Load and Clean ISI File A table of the records and a table of all records with unique ISI ids will appear in the Data Manager In this file each original record now has a Cite Me As attribute that is constructed from the first author PY J9 VL BP fields of its ISI record and will be used when matching paper and reference records To extract the paper citation network select the 361 Unique ISI Records table and run Scientometrics gt Extract Directed Network using the parameters 47 MI Extract Directed Network x Given a table this algorithm creates a directed network by placing a directed edge between the values in a given column to the values of a different column Source Column Cited References 3 Target Column cite Me As 2 Text Delimiter 3 Aggregate Function File C Documents and Settings katy Desktop nwb sampledata scientometrics properties isiPaperCitation properties 3 OK Cancel The result is a directed network of paper citations in the Data Manager Each paper node has two citation counts The local citation count LCC indicates how often a paper was cited by papers in the set The global citation count GCC equals the times cited TC value in the original ISI file P
120. pics and geo locations for authors and their institutions over time can be computed Derived networks are examined to count the number of papers or co authors per author the number of citations per paper or journal etc but also to determine the strength or success of co author inventor investigator relations etc The geospatial and topic distribution of funding input amp research output the structure and evolution of research topics evolving research areas e g based on young yet highly cited papers or the diffusion of information people or money over geospatial and topic space can be studied 7 1 2 4 Ordination Ordination techniques such as triangulation or force directed placement take a set of documents their similarities distances and parameters and generate a typically 2 dimensional layout that places similar documents closer together and dissimilar ones further apart Note that the table covers measures and algorithms commonly used in bibliometrics scientometrics research yet few of the new temporal geospatial topical and network based approaches in existence today 7 1 2 5 Display Analysis results can be communicated via text tables charts maps that are printed on paper or explored online to name just a few options Steps 3 5 will be discussed separately for e Temporal analyses in section 7 3 e Geospatial analyses in section 7 4 e Topical analyses in section 7 5 and e Network analyses in section 7 6 2 Bibl
121. r cornflo fixed false height 10 0 image label Medici labelcolor 0 0 0 Information Window labelsize 12 labelvi False T name ng Lo priorates 53 Color stroke cadetb Q Y pees style 2 totalities 54 Add visible true Ne Modify Field wealth 103 Ay Copy as Variable width 10 0 x 90 625 y 44 312 oh Object Property labe size Operator Value Colour Show Hide Size Show Label Hide Label Change Label Format Node Labels Format Edge Labels Node Shape Center Change History Resize Linear Colorize Nodes labelsize w From To Do Resize Linear Interpreter Graph Modifier Figure 4 16 GUESS Information Window visualization window and Graph Modifier window 4 6 3 1 Network Layout and Interaction GUESS provides different layout algorithms under menu item Layout Apply Layout gt GEM to the Florentine network Use Layout gt Bin Pack to compact and center the network layout Using the mouse pointer hover over a node or edge to see its properties in the Information window Right click on a node fo center on color remove Modify Field a node see Figure 4 16 Interact with the visualization as follows e Pan simply grab
122. ral and man made interactions It builds upon social network analysis Carrington Scott amp Wasserman 2005 Scott 2000 Wasserman amp Faust 1994 physics Barabasi 2002 information science Borner et al 2007 bibliometrics Borgman amp Furner 2002 Nicolaisen 2007 scientometrics webometrics Narin amp Moll 1977 White amp McCain 1998 informetrics Wilson 2001 webometrics Thelwall Vaughan amp Bjorneborn 2005 communication theory Monge amp Contractor 2003 sociology of science Lenoir 2002 and several other disciplines Authors institutions countries as well as words papers journals patents funding etc are represented as nodes and their complex interrelations as edges For example author and paper nodes exist in a delicate ecology of evolving networks Given a set of papers diverse networks can be extracted Typically three types of linkages are distinguished direct linkages e g paper citation linkages co occurrences e g of words authors references and co citations e g of authors or papers Linkages may be among units of the same type e g co authorship linkages or between units of different types e g authors produce papers Units of the same type can be interlinked via different link types e g papers can be linked based on co word direct co citation or bibliographic coupling analysis Linkages might be directed and or weighted Nodes and their linkages can be represented as a
123. rality Global Connected Components HITS Weak Component Clustering Blondel Community Detection W15T Pathfinder Network Scaling Extract K Core Annotate E Coreness Weighted and Directed HITS Unweighted and Directed Mode Indegree Mode Sutdegree Indegree Distribution Outdegree Distribution K Nearest Neighbor Single Node In Qut Degree Correlations PageRank HITS Dyad Reciprocity Arc Reciprocity Adjacency Transitivity Weak Component Clustering Strang Component Clustering Blondel Community Detection Extract K Core Annotate E Coreness Search Weighted PageRank Can Chord K Random walk Random Breadth First Fast Pathfinder Network Scaling Blondel Community Detection Burst Detection Discrete Network Dynamics k Extract and Annotate attractors Figure 1 5 Analysis menu and submenus 2 Sample Datasets and Supported Data Formats 2 1 Sample Datasets The yournwbdirectory sampledata directory provides sample datasets from the biology network scientometrics and social science research domains see listing below biology F1yMINT nwb humanprot nwb TF DNA regulonDB nwb WI5 nwb WormMint nwb YeastMINT nwb biology DND Used in DND model colorectalCancerModel csv drosophila csv graduationUseCase csv samplePolynomialUseCase csv SimpleDNDFunction csv network convertGraph v0 8 0 graphml kidscontest net netsci06 conference net seiyu graphml xml scientometrics
124. ree 200 200 200 0 0 0 dy SIAN gt gt gt for e in g edges lt P a e color 127 193 65 255 Y Wa ses FA gt gt gt M Interpreter f Graph Modifier Fi re Select a state w CIGIS a Figure 7 13 Directed unweighted paper paper citation network for FourNetSciResearchers dataset with all papers and references in the GUESS user interface left and a pruned paper paper citation network after removing all references and isolates right The complete network can be reduced to papers that appeared in the original ISI file by deleting all nodes that have a GCC of 1 Simply run Preprocessing gt Extract Nodes Above or Below Value with parameter values Extract from this number Below leave unchecked Numeric Attribute globalCitationCount The resulting network is unconnected 1 e it has many subnetworks many of which have only one node These single unconnected nodes also called isolates can be removed using Preprocessing gt Delete Isolates Deleting isolates is a memory intensive procedure If you experience problems at this step you may wish to consult the FAQ entitled How do I increase the amount of memory available to Network Workbench at https nwb slis indiana edu community n Main FA The FourNetSciResearchers dataset has exactly 65 isolates Removing those leaves 12 networks shown in Figure 6 right using the same color and size coding as in Figure 5 left Using
125. ree 0 0 1l jar nodeoutdegree 0 0 1 jar strongcomponentclustering 0 0 1 jar undirectedknn 0 0 1 jar kKcore A00 Jar multipartite oining 1 0 0 Jar onepointcorrelations 1 0 0 jar oOutdegreedistribution 1 0 0 jar pagerank pagerank reciprocity arc_1 0 0 jar reciprocity dyad_1 0 0 jar sampling_ 0 jar 1 0 0 jar 1 0 0 jar weighted_0 0 1 jar 1 0 0 jar selfloops_1 0 0 jar shortestpathdistr_1 0 0 jar sitebetweenness 1 0 0 jar symmetrize 1 0 0 jar averageclusteringcoefficient 0 6 0 jar averagenearestneighbor 0 6 0 jar degreeaveragestrength 0 6 0 jar degreestrength 0 6 0 jar endpointdegree 0 6 0 jar randomweight 0 6 0 jar strengthdistribution 0 6 0 Jar weightdistribution 0 6 0 jar iv attacktolerance 1 0 0 jar 1v errortolerance 1 0 0 jar iv search p2p bfs_1 0 0 jar iv search p2p randomwalk 1 0 0 jar Uu HU iu Lu edu edu edu edu 3 4 3 Modeling edu edu edu edu edu iu P KIS iu iu nwb nwb nwb nwb nwb modeling modeling modeling modeling modeling barabasialbert 1 0 0 jar discretenetworkdynamics 1 0 0 jar erdosrandomgraph 1 0 0 jar smallworld 1 0 0 jar weighted evolvingnetwork 1 0 0 jar p2p can_1 0 0 jar p2p chord_0 4 0 jar iv modeling p2p hypergrid_0 4 0 Jar iv modeling p2p pru 0 4 0 jar iv modeling tarl 0 4 0 jar iv modeling iv modeling J Jii iu io iu edu edu edu edu edu 13 3 4 4 Visualizat
126. rete Network Dynamics DMD le Li g Tree view prefuse beta Evolving Network Weighted Balloon Graph prefuse alpha Snowball Sampling n nodes Force Directed with Annotation prefuse beta Kamada Kawai JUNG Fruchterman Reingold JUNGI Symmetrize Fruchterman Reingold with Annotation prefuse beta Dichotomize Spring JUNG Multipartite Joining Small World prefuse alpha Normalize Text Slice Table by Time Preferences Mode Sampling Edge Sampling Parallel Coordinates dena LaNet Circular Hierarchy Figure 1 4 File Preprocessing Modeling and Visualization menus The Analysis menu has several submenus as shown in Figure 1 5 Analysis Network Analysis Toolkit MAT Unweighted and Undirected Weighted and Undirected Unweighted and Directed Weighted and Directed Search Discrete Network Dynamics Textual Weighted and Undirected Clustering Coefficient Nearest Neighbor Degree Strength vs Degree Degree amp Strength Average Weight vs End point Degree K Nearest Neighbor Java Strength Distribution Weight Distribution Randomize Weights M5T Pathfinder Network Scaling Fast Pathfinder Network Scaling Blondel Community Detection Unweighted and Undirected Node Degree Degree Distribution Watks Strogatz Clustering Coefficient Watts Strogatz Clustering Coefficient over K Diameter Average Shortest Path Shortest Path Distribution Mode Betweenness Cent
127. rner Katy Soma Sanyal Alessandro Vespignani 2007 Network Science In Blaise Cronin Ed Annual Review of Information Science amp Technology Vol 41 pp 537 607 Medford NJ Information Today Inc American Society for Information Science and Technology Bornmann Lutz 2006 H Index A New Measure to quantify the Research Output of Individual Scientists http www forschungsinfo de ig agora H Index h index asp accessed on 7 17 08 Bosman Jeroen Ineke van Mourik Menno Rasch Eric Sieverts Huib Verhoeff 2006 Scopus Reviewed and Compared The Coverage and Functionality of the Citation Database Scopus Including Comparisons with Web of Science and Google Scholar Utrecht University Library Brandes Ulrik 2001 A Faster Algorithm for Betweeness Centrality Journal of Mathematical Sociology 25 2 163 177 73 Brandes Ulrik Dorothea Wagner 2008 Analysis and Visualization of Social Networks http visone info accessed on 7 15 08 Brin S L Page 1998 The Anatomy of a Large Scale Hypertextual Web Search Engine Paper presented at the Proceedings of the Seventh International Conference on World Wide Web 7 Brisbane Australia pp 107 117 Callon M J P Courtial W Turner S Bauin 1983 From Translations to Problematic Networks An Introduction to Co Word Analysis Social Science Information 22 191 235 Callon M J Law A Rip Eds 1986 Mapping the Dynamics of Science and Technology London
128. s ainberg A a kuj e Mark Ratner o yemes Sethna LPM cisco alero 18 aj a Stephen lJ kolboj John SGuckenhelr Keashav F Daniel Ralph Abruna Neseph Halpern we Fried Eva Tamme ang LZ Ramakrish kenneth Brown Sane Bok Edward Swartz eo Durrett Philip Protter ST fus oste 18 Gennady Samorodnitsky Sidnev Resnick Figure 7 5 Largest component of Cornell University co PI network Node size and color encode the total award amount The top 50 nodes with the highest total award amount are labeled 7 2 4 Scholarly Database Medline U S patent as well as funding data provided by the National Science Foundation and the National Institutes of Health can be downloaded from the Scholarly Database SDB at Indiana University SDB supports keyword based cross search of the different data types and data can be downloaded in bulk see Figures 7 6 and 7 7 for interface snapshots Register to get a free account or use Email nwb indiana edu and Password nwb to try out functionality Search the four databases separately or in combination for Creators authors inventors investigators or terms occurring in Title Abstract or All Text for all or specific years If multiple terms are entered in a field they are automatically combined using OR So breast cancer matches any record with breast or cancer in that field You
129. s complex networks and metabolic networks starting in the early 2000s and not being finished at the end of the dataset as opposed to ending in 2005 the last year of the dataset but for three papers Another pattern is that almost all of the authors with bursts in the dataset were graduate students of Wasserman Vespignani or Barabasi during this period One notable example of this is Reka Albert who bursts from 1997 to 2000 corresponding to work on her Ph D with Barabasi The result can be visualized as a chart with word and time dimensions see Figure 7 9 Bursts for each word are shown as horizontal red bars across the time dimension Bursts with strength above 10 are colored a darker red All bursts are shown for authors and keywords but only words in the fifteen most powerful bursts are shown for cited references and terms in the abstract For each type of word words are sorted by the start of their first burst and the end of their last burst In the burst for abstract terms stop words are removed This chart was made in MS Excel by following the steps enumerated to create Figure 7 8 with some minor modifications for display 42 Authors Burst Strength Wasserman S 14 55324339 Galaskiewicz J 3 5849977 Iacobucci D 31803135185 Wicsek T 3 558125 Pjetronera L 02534576 Haslin 5 4 75039904 Stanley HE 7 38102464 Loreto W 31921303815 apperi 5 212034593594 Daruka I 3159946430 Albert R 644201572 Jeong H 374294521
130. s point to a page and hub value is determined by how authoritative its links are HITS can be used effectively on more general network problems such as social biological or scholarly networks The network can be directed or undirected weighted or un weighted The output is a network that in addition to the original node attributes has two extra floating point attributes authority_score and hub_score In the NWB Tool this very algorithm can be found in Unweighted amp Undirected Unweighted amp Directed Weighted amp Undirected or Weighted amp Directed in Analysis Here we apply it to the FourNetSciResearchers paper citation network The top ten nodes with the highest authority_score and hub_score are listed below authority score Paper 0 05984392 Albert R 2002 REV MOD PHYS V74 P47 0 026528405 Vazquez A 2002 PHYS REV E V65 DOI ARTN 066130 0 025031997 Ravasz E 2003 PHYS REV E V67 DOI ARTN 026112 0 022470286 Colizza V 2005 PHYSICA A V352 P1 0 02229378 Barabasi AL 2002 PHYSICA A V311 P590 0 021960443 Pastor satorras R 2001 PHYS REV E V6306 DOI ARTN 066117 0 021393541 Moreno Y 2002 EUR PHYS J B V26 P521 0 021259034 Barabasi AL 2004 NAT REV GENET V5 P101 0 020125171 Vazquez A 2003 PHYS REV E V67 DOI ARTN 046111 0 019402143 Pastor satorras R 2002 PHYS REV E V65 DOI ARTN 036104 60 hub_score 0 017088737 0 014638619 0 010365143 0 010319315 0 010109196 0
131. scientometrics bibtex LaszloBarabasi bib bibsonomy bib scientometrics csv LaszloBarabasi csv scientom trios endnote LaszloBarabasi enw selentometrics isi FourNetSciResearchers isi LaszloBarabasi isi EugeneGarfield isi AlessandroVespignani isi StanleyWasserman isi test5papers isi ThreeNetSciResearchers isi scientometrics models TARL Agingfunction txt inscript tarl scientometrics nsf BethPlale nsf Cornell nsf GeoffreyFox nsf Indiana nsf MichaelMcRobbie nsf Michigan nsf scientometrics properties Used to extract networks and merge data bibtexCoAuthorship properties endnoteCoAuthorship properties isiCoAuthorship properties isiCoCitation properties isiPaperCitation properties mergeBibtexAuthors properties mergeEndnoteAuthors properties mergelsiPaperCitation properties mergelsiAuthors properties mergeNsfPIs properties mergeScopusAuthors properties nsfCoPI properties nsfPIToProject properties scopusCoAuthorship properties scientometrics scopus BrainCancer scopus socialscience florentine nwb friendster graphml xml friendster xgmml xml PSYCHCONSULT nwb terror graphml xml terror xgmml xml 2 2 Data Formats In August 2009 the NWB Tool supports loading the following input file formats GraphML xml or graphml XGMML xml Pajek NET net Pajek Matrix mat NWB nwb TreeML xml Edgelist edge Scopus csv scopus NSF csv nsf CSV csv ISI 1s1
132. ses a very different approach compared to MST based pathfinder scaling The underlying idea of this approach is based on a classical algorithm in graph theory for shortest path computation called Floyd Warshall s Shortest Path Algorithm This leads to a reduction in the time complexity from O n to O n Also the space complexity is drastically reduced from 2 n matrices in the original algorithm to 2 matrices If a network has a low standard deviation for edge weights or if many of the edge weights are equal to the minimum edge weight then that network might not be scaled as much as expected This prevents unweighted networks from being scaled at all If the networks being processed are undirected then the MST based pathfinder network scaling algorithm can be used This will give results many times faster than fast pathfinder algorithm In the NWB Tool the algorithm can be found under Analysis gt Weighted and Un directed gt Fast Pathfinder Network Scaling The user has to provide the Value of R parameter the field that represents the edge weight and how the edge weight should be considered Dissimilarity or Similarity MM Fast Pathfinder Network Scaling Performs Fast Pathfinder Network Scaling Value of R parameter 1 0 V Value of R parameter Infinity Edge Weight Column number ofcoauthor edworks Weight to be represented as Similarity The provided network should not contain edge weights of l
133. sy to observe the growth of a network over time see the detailed explanation in Figure 7 28 70 Citations in a single time slice LW 1980 SCIENCE V208 P1095 Times cited Year of first co citation gt ir Se Time slice AO ORANG AR 1991 GEOLOGY V19 P867 2 Year of publication Figure 7 28 Temporal reference system size and color coding in Citespace II CiteSpace II highlights high betweenness centrality BC nodes also called pivotal points by purple circle borders This can be replicated in NWB Tool by coloring all nodes above a certain BC threshold value However Citespace II can also show the nodes with red colors whose citation has a sharp increase in certain time slice In Figure 7 24 some n odes are rather large and have a purple ring and red color An example is BARABASI AL 1999 SCIENCE V286 P5909 Using the function of Citation History in Citespace II the citation count history of this node can be plotted Figure 7 29 shows that this very node not only has high betweenness but also has the sharp increase on citation in certain time slices N LI N LI N 1488 1990 1992 1994 1996 1998 000 2002 2004 2006 Figure 7 29 Citation History of Node BARABASI AL 1999 SCIENCE V286 P5909 In the given example co authorship networks generated using NWB Tool appear to be easier to read while CiteSpace II renders word co citation networks in a more legible way Citespace II a
134. t cited paper h index Bornmann 2006 and number of papers and citations over time as calculated by the Web of Science by Thomson Scientific Thomson Reuters 2008b are given in Table 7 2 ISI formatted files of all papers including references were downloaded for all four researchers in December 2007 The superset of the four data files is called FourNetSciResearchers in the remainder of this tutorial Table 7 2 Names ages number of citations for highest cited paper h index and number of papers and citations over time as rendered in the Web of Science for four major network science researchers Name Age Total H Total Papers and Citations per Year for the last 20 Years Index Papers Cites ni e mem Eugene 82 1525 31 672 Published items in Each Year i Citations in Each Year Garfield ra 3 ii fi Ni i MI me i se fEZEEZEZECEZEZEEE Stanley 122 17 35 Published Items in Each Year Citations in Each Year Wasserman lu li alll ESS LES STEFERSLEL SEES EEEEEREEEEEETEZIZ Trees mL E tan Alessandro 42 451 33 101 ms in Citations in Each Year Vespignani T laj Albert 40 2218 47 126 Published jiems E Each Year Citations in Each Year L szl 16 3000 oe 147 2500 4 Barabasi 12 104 2000 84 1500 4 Repeated 41 16 920 52 159 5 IN 41 query on li 500 Sept 21 f KAN aa UNI 2008 MERASA oo LL s5 The table reveals that a high age i e more time for publishing typically results in more
135. t co citation co authorship word co occurrence networks and burst detection 7 8 3 1 Document Co Citation Network The document co citation network for the FourNetSciResearcher dataset was derived using the NWB Tool and CiteSpace see Figure 7 24 In both cases the sizes of the nodes stand for betweenness centrality Node color coding in NWB Tool was set to reflect betweenness centrality CiteSpace color codes nodes based on ten 2 year time slices covering 1988 2007 Top 10 of most cited references are selected 415 nodes and 7147 links are laid out in the network An alternative larger figure of a document co citation network derived via NWB Tool is given in section 7 6 3 1 Document Co Citation Network DCA 67 GRANOVET MS 1973 AM J SOCIOL WASSERMAN S 1994 SOCIAL NETWORK ANAL Figure 7 24 Document co citation network of FourNetSciResearchers with NWB Tool left and using CiteSpace II right 7 8 3 2 Author Co Occurrence Co Author Network Figure 7 25 shows the CiteSpace rendering of the co authorship network for the FourNetSciResearchers dataset using five 4 year time slices covering 1988 2007 In this network the top10 of most occurred authors from each slice are selected There are 249 nodes and 907 links in it Compare to NWB rendering in section 7 6 2 1 Author Co Occurrence Co Author Network pe E a j Ba rabasi AL a TI EL 1 j ri E Se Lu SUE 4 l da LA Sa os z LAS mH
136. ta Manager window You may right click on this network and choose View to look at the contents You may also open this file separately in a text editor to explore it or make changes Please note that as you work new files will be created in the Data Manager window You may choose to save these files in various formats and you will need to make sure that you have highlighted the network file that you wish to work in 6 2 Basic Network Properties As a matter of practice you may want to learn a little about your network and confirm that it was read correctly into NWB The Graph and Network Analysis Toolkit provides a quick overview of your network Analysis gt Network Analysis Toolkit If you run this on the PSYCHCONSULT network you will find that you have a directed network with 113 nodes and no isolated nodes There are two node attributes present the node label which is in this case the job title of all of the employees and the area in this network the unit in which the employee generally worked You can also see that you have 861 edges no self loops or parallel edges and no edge attributes A common edge attribute 1s weight value but this network is unweighted The network is weakly connected each node is connected to another node in the main component with no isolates It is not strongly connected however as some nodes are unreachable they send but do not receive ties You will also see the network s density
137. the yournwbdirectory plugins directory When the Tool is re started the plugin will no longer be available in the menu Users can also organize the menu items as they wish In order to do so you must open yournwbdirectory configuration default_menu xml in any text editor The file is shown in Figure 1 2 ie default menu xml Notepad mh mas File Edit Format View Help lt xml version 1 0 encoding UTF 8 7 gt lt lt xml version 1 0 encoding I50 8859 1 7 gt gt menu layout top menu name File menu pid org cishell reference gui persistence load FileLoad gt menu pid edu iu nwb composite 151 loadandclean ISILoadandc leanAlgorithm gt menu pid edu iu nwb preprocessing prefuse beta directoryhierarchyreader DirectoryHierarchyReadell menu name Datasets type group gt menu pid org mediavirus parvis sampledata pcvis_data txt gt lt menu gt menu type break gt menu pid org cishell reference gui persistence save Save gt menu type break gt menu pid org cishell reference qui persistence view Fi leview gt menu pid org cishell reference gui persistence viewwith Fi leviewwith gt menu type break gt menu pid A cat upto sr Ghar Ag menu pid edu iu nwb converter tablegraph GraphTable gt menu type break gt lt menu Tests type group gt i org cis ee ee ee o menu pid org
138. tometrics 73 1 53 78 De Roure D C Goble R Stevens 2009 The Design and Realisation of the myExperiment Virtual Reserach Environment for Social Sharing of Workflows Future Generation Computer Systems 25 561 567 http eprints ecs soton ac uk 15709 accessed on 6 22 2009 Deerwester S S T Dumais G W Furnas T K Landauer R Harshman 1990 Indexing by Latent Semantic Analysis Journal of the American Society for Information Science 41 391 407 Ding Ying Erija Yan Arthur Frazho James Caverlee 2009 PageRank for Ranking Authors in Co Citation Networks Journal of the American Society for Information Science and Technology 9999 9999 1 15 Elnashai Amr Bill Spencer Jim Myers Terry McLaren Shawn Hampton Jong Sung Lee Chris Navarro Nathan Tolbert 2008 Architectural Overview of MAEviz HAZTURK Journal of Earthquake Engineering 12 S2 92 99 Erdos P A Renyi 1959 On Random Graphs I Publicationes Mathematicae Debrecen 6 290 297 Feder Alexander 2006 BibTeX org Your BibTeX resource http www bibtex org accessed on 7 15 08 Fekete Jean Daniel Katy Borner chairs Eds 2004 Workshop on Information Visualization Software Infrastructures Austin Texas Fingerman Susan 2006 Electronic Resources Reviews Web of Science and Scopus Current Features and Capabilities Issues in Science and Technology Librarianship Fall http www istl org 06 fall electronic2 html accessed on 9 23
139. ture and CIShell algorithm architecture http cishell org built on top of OSGi make this possible Cytoscape is also adopting an architecture based on OSGi though it will still have a specified internal data model and will not use CIShell in the core Moving to OSGi will make it possible for the tools to share many algorithms including adding Cytoscape s visualization capabilities to Network Workbench Recently a number of other efforts adopted OSGi and or CIShell Among them are e Cytoscape http www cytoscape org lead by Trey Ideker UCSD is an open source bioinformatics software platform for visualizing molecular interaction networks and integrating these interactions with gene expression profiles and other state data Shannon et al 2002 e Taverna Workbench http taverna sourceforge net lead by Carol Goble University of Manchester UK is a free software tool for designing and executing workflows Hull et al 2006 Taverna allows users to integrate many different software tools including over 30 000 web services from many different domains such as chemistry music and social sciences The myExperiment http www myexperiment org social web site supports finding and sharing of workflows and has special support for Taverna workflows De Roure Goble amp Stevens 2009 Currently Taverna uses Raven at its core but a reimplementation using OSGi is underway e MAEviz https wiki ncsa uiuc edu display MAE Home managed by Shawn
140. ve analysis requires access to more than one database see also Bosman van Mourik Rasch Sieverts amp Verhoeff 2006 de Moya Anegon et al 2007 Fingerman 2006 Meho amp Yang 2007 Nisonger 2004 Pauly amp Stergiou 2005 In the NWB Tool load yournwbdirectory sampledata scientometrics isi barabasi1 1s1 kvournwbdirectory sampledata scientometrics scopus barabas1 scopus yournwbdirectory sampledata scientometrics bibtex barabasi bib downloaded from Google Scholar 35 is also available in the respective subdirectories in yvournwbdirectory sampledata scientometrics It is interesting to compare the result set retrieved from ISI Scopus and Scholar Google 7 2 2 Personal Bibliographies EndNote and Bibtex Personal references collected via reference management software such as EndNote Thomson Reuters 2008a Reference Manager The Thomson Corporation 2008 or the Bibtex format Feder 2006 can also be read Sample datasets are included in scientometrics sampledata bibtex or endnote Simply load the file and a csv file with all unique files will appear in the Data Manager 7 2 3 Funding NSF Data Funding data provided by the National Science Foundation NSF can be retrieved via the Award Search site http www nsf gov awardsearch Search by PI name institution and many other fields see Figure 7 3 Mul ward Search Search All fields Windows Internet Explorer Po Se E E 1
141. version Help E console u la o Data Manage Welcome to the Network Workbench NWB tool that supports the preprocessing modeling analysis LAJ NWB file C Jappsinwb 0 9 0 sampledatainetworkiflorentine nwb and visualization of small medium and large networks The Network Workbench project is supported in part by the NSF II5 0513650 award The primary investigators are Dr Katy Borner Dr Albert L szl Barab si Dr Santiago Schnell Dr Alessandro Vespignani Dr Stanley Wasserman Dr Eric A Wernert and Dr Craig Stewart The NWB tool was developed by Weixia Bonnie Huang Dr Santo Fortunato Bruce Herr Russell Duhon Timothy Kelley Micah Walter Linnemeier Duygu Balcan Heng Michael Zhang Mariano Beir Megha Ramawat Ben Markines M Felix Terkhorn Cesar A Hidalgo R Dr Soma Sanyal Ann McCranie Ramya Sabbineni Yivek 5 Thakres Dr Alessandro Vespignani and Dr Katy Barner It builds on the Cyberinfrastructure Shell http cishell org developed at the Information Visualization Laboratory http f ivl slis indiana edu and the Cyberinfrastructure for Network Science Center both at Indiana University For more information on the Network Workbench project see http nwb slis indiana edu and https f nwb slis indiana edu community Load was selected Loaded C apps nwb 0 9 0 sampledatajnetwork florentine nwb E scheduler Remove From List Remove completed automatically Remov
142. w York Academy of Sciences Huang Weixia Bonnie Bruce Herr Russell Duhon Katy Borner 2007 Network Workbench Using Service Oriented Architecture and Component Based Development to Build a Tool for Network Scientists Paper presented at the International Workshop and Conference on Network Science Hull Duncan Katy Wolstencroft Robert Stevens Carole Goble Mathew R Pocock Peter Li Tom Oinn 2006 Taverna A Tool for Building and Running Workflows of Services Nucleic Acids Research 34 Web Server Issue W729 W732 http nar oxfordjournals org cgi content abstract 34 suppl 2 W729 accessed on 6 22 2009 Ihaka Ross Robert Gentleman 1996 R A language for data analysis and graphics Journal of Computational and Graphical Statistics 5 3 299 314 http www amstat org publications jcgs accessed on 7 17 08 Jaro M A 1989 Advances in record linking methodology as applied to the 1985 census of Tampa Florida Journal of the American Statistical Society 64 1183 1210 Jaro M A 1995 Probabilistic linkage of large public health data file Statistics in Medicine 14 491 498 Johnson Brian Ben Schneiderman 1991 October 22 25 Tree Maps A Space Filling Approach to the Visualization of Hierarchical Information Structures Paper presented at the 2nd International IEEE Visualization Conference San Diego CA pp 284 291 Kampis G L Gulyas Z Szaszi Z Szakolczi 2009 Dynamic Social Networks and the TEXTrend
143. well as a report describing which nodes were merged The updated co author network can be visualized using Visualization gt GUESS see the above explanation on GUESS Figure 7 15 shows a layout of the combined FourNetSciResearchers dataset after setting the background color to white and using the command lines gt resizeLinear numberofworks 1 50 gt colorize numberofworks gray black for n in g nodes n strokecolor n color border color same as its inside color resizeLinear numberofcoauthoredworks 25 8 colorize numberofcoauthoredworks 127 193 65 255 V black VVV Va nodesbynumworks g nodes make a copy of the list of all nodes def bynumworks nl n2 define a function for comparing nodes 2 return cmp nl numberofworks n2 numberofworks gt Ea sort bynumworks sort list nodesbynumworks reverse reverse sorting list starts with highest gt for i in range 0 50 nodesbynumworks i labelvisible make labels of most productive authors visible true 5I Alternatively run GUESS File gt Run Script and select yournwbdirectory sampledata scientometrics isi co author nw py That is author nodes are color and size coded by the number of papers per author Edges are color and thickness coded by the number of times two authors wrote a paper together The remaining commands identify the top 50 authors with the most papers and make their name labels visible
144. which t a network can be saved The tool includes a plugin accessible at File gt Tests gt Converter Graph which generates a directed graph of the 29 converters that convert among 22 different formats see Figure 1 Nodes are weighted depending upon how many times they participate in a converter relationship 0 o o file teetivist rau herbe ey gpuir prefuse gaph bah o edu berkeley guirprefuse graph JJ eds jung ge aph Graph se data A e aii o flete bitas h da sored 7 j N data Graph o filecteortyineferbebs E lector cre Filebertigraphri semi o file tetp Z o filetextitdge in ral image Bouffe redige O Alt amplicon pajekmet LJ files pg lde def eapon refuze data Table fle appligatronpapekrmat Figure 4 5 Converter graph More information can be found at https nwb slis indiana edu community n File ConverterGraph and https nwb slis indiana edu community n CustomFillings DataConversionService 4 3 Compute Basic Network Statistics It is often advantageous to know for a network Whether it is directed or undirected Number of nodes Number of isolated nodes A list of node attributes Number of edges Whether the network has self loops if so lists all self loops 16 Whether the network has parallel edges if so lists all parallel edges A list of edge attributes Average degree Whether the graph is weakly connected Number of weakly connected components Number o
145. y one parent node Nodes which have no children are called leaf nodes All other nodes are referred to as intermediate nodes This section introduces different algorithms to visualize tree data using tree views tree maps radial tree graph and balloon graph layouts 4 5 1 Tree View Visualization The tree view layout places the root node on the left of the canvas First level nodes are placed on an imaginary vertical line to the right of it Second level nodes are placed on an imaginary vertical line left of the first level nodes etc In the NWB Tool select a tree dataset e g generated using the Directory Hierarchy Reader in the Data Manager window then use Visualization gt Tree View prefuse beta and a window similar to Figure 1 will appear displaying the Tree View visualization If you press and hold the right or middle button of the mouse while moving it back and forth you can zoom in and out on the Tree By clicking a folder name such as sampledata all sub folders and files inside the sampledata folder will display Use the search box in the bottom right corner to enter a search terms and matching files will highlight Bree Yiew prefuse beta I E Of x mi WormMint nwb YeastMINT nwb DND convertGraph v0 8 0 graphml florentine nwb friendster graphml xml friendster xgmml xml humanprot nwb kidscontest net netsci06 conference net PSYCHCONSULT nwb seiyu graphml xml terror graphml xml terror xgmml xml wi
146. youts of very large graphs e Ability to add new vertices to a previously drawn graph This is one of the few force directed layout algorithms that can scale to over 1 million nodes making it ideal for large graphs However small graphs hundreds or less do not always end up looking good The algorithm expects similarity networks as input Distance and other networks will have to be converted before they can be laid out The version of DrL included in NWB only does the standard force directed layout no recursive or parallel computation DrL expects the edges to be weighted directed edges where the weight greater than zero denotes how similar the two nodes are higher is more similar The NWB version has several parameters The edge cutting parameter expresses how much automatic edge cutting should be done 0 means as little as possible 1 as much as possible Around 8 is a good value to use The weight attribute parameter lets you choose which edge attribute in the network corresponds to the similarity weight The X and Y parameters let you choose the attribute names to be used in the returned network which corresponds to the X and Y coordinates computed by the layout algorithm for the nodes DrL can be very useful for large scale similarity computations such as co citation and co word analyses In NWB the results can be viewed in either GUESS or Visualization gt Specified prefuse alpha For more information see https nwb slis ind

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