Mastering Gephi Network Visualization Produce advanced network graphs in Gephi and gain valuable insights into your network datasets Ken Cherven BIRMINGHAM - MUMBAI Mastering Gephi Network Visualization Copyright © 2015 Packt Publishing All rights reserved No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews Every effort has been made in the preparation of this book to ensure the accuracy of the information presented However, the information contained in this book is sold without warranty, either express or implied Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals However, Packt Publishing cannot guarantee the accuracy of this information First published: January 2015 Production reference: 1220115 Published by Packt Publishing Ltd Livery Place 35 Livery Street Birmingham B3 2PB, UK ISBN 978-1-78398-734-4 www.packtpub.com Credits Author Ken Cherven Reviewers Project Coordinator Leena Purkait Proofreaders Ladan Doroud Cathy Cumberlidge Miro Marchi Paul Hindle David Edward Polley Samantha Lyon Mollie Taylor George G Vega Yon Commissioning Editor Ashwin Nair Acquisition Editor Sam Wood Content Development Editor Amey Varangaonkar Technical Editors Shruti Rawool Shali Sasidharan Copy Editors Rashmi Sawant Stuti Srivastava Neha Vyas Indexer Monica Ajmera Mehta Graphics Abhinash Sahu Production Coordinator Conidon Miranda Cover Work Conidon Miranda About the Author Ken Cherven is a Detroit-based data visualization and open source enthusiast, with 20 years of experience working with data and visualization tools In addition to Gephi, he has worked with a variety of open source tools, including MySQL, SpagoBI, JasperServer, D3, Protovis, Omeka, QGIS, Leaflet, and Exhibit He also has considerable experience using corporate software tools from Microsoft, Cognos, Tableau, and Oracle An automotive analyst and visualizer by day, he spends much of his personal time turning baseball data into web-based visualizations housed on his website, http:// visual-baseball.com He has previously authored Network Graph Analysis and Visualization with Gephi, Packt Publishing, as well as a self-published book, MLB Pennant Races, 1901-1968: A Visual Analysis of Baseball's Pennant Races, Visual-Baseball Press His current areas of interest include visual dashboards, interactive networks, and anything involving geographic information Acknowledgments I would like to thank the members of my family for their patience and understanding over the course of several months spent working on this book This always starts with my wife, Karen, and extends to my children, Kellen, Kristopher, and Katie, as well as my always helpful mother-in-law, Carole Young This book would not have been possible without the considerable efforts of a group of thorough technical and content editors I would like to sincerely thank Mollie Taylor, Ladan Doroud, Miro Marchi, Ted Polley, George Vega Yon, Marta Castellani, and Manasi Pandire for their considerable efforts to make this the best possible book All of your input has been noted, and many improvements have been incorporated A special thanks also to Amey Varangaonkar at Packt Publishing for managing the entire process while also making recommendations that will result in a more enjoyable reading experience Thanks also to others who helped in the early stages by providing useful feedback to get the book started This list includes Joanne Fitzpatrick and Richard Gall at Packt Publishing, plus Gephi community members, Randy Novak, Mike Hughes, Matthieu Totet, Marco Valli, Gerry Wilson, and Carlos Benito Amat Finally, I would like to thank the creators and maintainers of Gephi for providing such a powerful tool that allows users to explore the fascinating world of network science Thanks also to the growing community of enthusiasts who use Gephi to create some remarkable visualizations My hope is that this book will make it easier for you to tap into the power of Gephi and, perhaps, even provide a few new approaches to leverage this powerful tool About the Reviewers Ladan Doroud is a PhD candidate at the University of California, Davis She received her master's degree in computer science from the same university in 2013 She is currently working on her PhD in computer science in Prof Eisen's lab as a computational biologist and data scientist Her research interests mainly lie in the area of large-scale network analysis, clustering and data mining with special focus on community detection, and function prediction of protein sequences in large-scale biological networks She has an extensive background in learner-centered education, including her collaboration with Udacity, Inc in 2014 as a course manager on the data science track, as well as her collaboration with the California State Summer School for Mathematics and Science (COSMOS) in 2011 She can be reached at ldoroud@ucdavis.edu Miro Marchi is a PhD candidate at the University of Verona, Italy He received his master's degree in cultural anthropology, ethnology, and ethnolinguistics from Ca' Foscari University of Venice in 2010 He has authored Self-Governance Lessons from Bali and Stephen Lansing, Cangiani M (ed.), Alternative Approaches to Development, Cleup, 2012, where he has reviewed the research of the interdisciplinary team coordinated by the anthropologist, Stephen J Lansing, on farmers' cooperation network for rice cultivation in Bali His current research focuses on finding practical ways to foster the emergence of self-organization in social-economic networks He is applying ethnographic methods coupled with community-based online network visualization, which is built with Drupal and D3 and available at www.retebuonvivere.org/rete, and he is interested in the use of complexity theory for sustainability and the commons He can be reached at miro.marchi@gmail.com David Edward Polley is a social sciences librarian at Indiana University-Purdue University Indianapolis (IUPUI) Prior to joining IUPUI, he worked as a researcher at the Cyberinfrastructure for Network Science Center in the Indiana University School of Informatics and Computing, Bloomington He is interested in the various ways people use data, generated in social science research He is the coauthor of a book on data visualization with Dr Katy Börner titled, Visual Insights: A Practical Guide to Making Sense of Data Mollie Taylor is the President of Proximity Viz LLC, located in Atlanta, Georgia, USA, which provides data visualization and mapping services to a wide range of clients She holds degrees in economics and international affairs from the Georgia Institute of Technology Her blog on programming for data analysis can be found at http://blog.mollietaylor.com/ George G Vega Yon is currently a PhD student at the California Institute of Technology He holds a BA degree in business administration and an MA degree in economics and public policy from Adolfo Ibáñez School of Government (Chile) He is the author of several R and Stata modules, including ABCoptim: Implementation of Artificial Bee Colony (ABC) Optimization, rgexf: an R package to work with GEXF graph files, and Introducing PARALLEL: Stata module for parallel computing He has shown a deep interest in statistical computing and data visualization; furthermore, he is the founder of the Chilean R-Users Group (useR) He is the cofounder of the entrepreneurship, NodosChile.org Social Network Analysis, one of the first companies in Chile to put the eye on applied SNA analysis George's scholarly interests are focused on policy analysis, complexity and statistical computing—recognized by the community, as he has served as a reviewer of the Journal of Computational Economics www.PacktPub.com Support files, eBooks, discount offers, and more For support files and downloads related to your book, please visit www.PacktPub.com Did you know that Packt offers eBook versions of every book published, with PDF and ePub files available? 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PacktLib is Packt's online digital book library Here, you can search, access, and read Packt's entire library of books Why subscribe? • Fully searchable across every book published by Packt • Copy and paste, print, and bookmark content • On demand and accessible via a web browser Free access for Packt account holders If you have an account with Packt at www.PacktPub.com, you can use this to access PacktLib today and view entirely free books Simply use your login credentials for immediate access Table of Contents Preface 1 Chapter 1: Fundamentals of Complex Networks and Gephi Graph applications Collaboration graphs Who-talks-to-whom graphs Information linkages Technological networks Natural-world networks A network graph analysis primer Paths and connectivity 8 9 9 10 11 Paths 11 Cycles 12 Connectivity 13 Network structure 13 Network behaviors 20 Overviewing Gephi Primary windows Data laboratory 22 23 23 Centrality 14 Components 17 Giant components and clustering 18 Homophily 19 Density 19 Contagion and diffusion Network growth Manual entry CSV import Excel import MySQL import Graph file import 20 21 24 24 25 25 25 Appendix • Kourtellis, N., Alahakoon, T., Simha, R., Iamnitchi, A., Tripathi, R Identifying high betweenness centrality nodes in large social networks: Social Network Analysis and Mining 899-914 Retrieved November 2, 2014 • Lattanzi, S., Sivakumar, D Affiliation networks 2009 • Newman, M The Structure and Function of Complex Networks, SIAM Review 2003 167-167 Retrieved March 27, 2014 • Steen, M Graph Theory and Complex Networks: An Introduction 2010 • Tang, J., Musolesi, M., Mascolo, C., Latora, V Temporal distance metrics for social network analysis 2009 Retrieved March 21, 2014 • Wimmer, A., Lewis, K Beyond and below racial homophily: ERG models of a friendship network documented on Facebook American Journal of Sociology 2010 583-642 Retrieved June 22, 2014 [ 347 ] Index A C Attractive and Repulsive Forces (ARF) layout about 36, 61, 71, 93 General attraction force 72 Neighbor attraction force 71 Precision force 72 Repulsive force 72 testing 97-100 attribute-based DNA about 262 data, preparing 262, 263 dynamic attribute networks, implementing 264-273 dynamic attribute networks, viewing 264-273 attributes filter about 152 Equal function 152 Inter Edges 152 Intra Edges 152 Non-null condition 153 Partition Count filter 153 Partition filter 153 Range filter condition 153 average path length, network measures 183 average path length, network statistics 200 Center for Computational Analysis of Social and Organizational Systems (CASOS) URL 346 Center for Network Science (CNS) URL 346 centrality 14, 15 centrality measures, graph statistics about 185, 186 betweenness centrality 188 closeness centrality 187 degree centrality (undirected graphs) 186 eigenvector centrality 187 in-degree centrality (directed graphs) 187 out-degree centrality (directed graphs) 187 centrality statistics betweenness centrality 204, 205 closeness centrality 202, 203 degree centrality 201, 202 eigenvector centrality 203, 204 centrality statistics, interpreting about 193 betweenness centrality 195 closeness centrality 194 degree centrality 193 eigenvector centrality 195 in-degree centrality 194 out-degree centrality 194 Chinese Whispers algorithm 221 Chinese Whispers plugin using 233-237 Circular layout about 35, 78, 79 Concentric layout 80 Dual Circle layout 80, 81 B Barabasi-Albert scale free model 128 betweenness centrality 15, 188, 195, 204, 205 bibliography 346, 347 bipartite graph 85 brush function 49 closeness centrality 187, 194, 202, 203 cluster analysis 123 clustering 29, 122-124 clustering and neighborhood measures, graph statistics about 188 clustering coefficient 188 Link Communities 189 modularity 189 neighborhood overlap and embeddedness 189 number of triangles 189 clustering statistics about 205 clustering coefficient 205 embeddedness 207 Link Communities 206 modularity 206 number of triangles 206 clustering statistics, interpreting about 196 clustering coefficients, interpreting 196 embeddedness 197 Link Communities statistic 197 modularity statistic 197 number of triangles 197 CmapTools URL 116 community detection 219 complex filters about 168 INTERSECTION filter 175-177 Mask, working with 175-177 multiple filter conditions, applying 168, 169 subfilters, using 169-174 UNION operator 178, 179 Concentric layout about 35, 80, 93 testing 101, 102 connected components, network measures 184 connected graph 17 connections 10 connectivity 13 contagion about 11, 114-117 SIR model 117 network, viewing 131-135 CSV files 281 CSV import, data laboratory 24 cycles 12 D data formatting, for Gephi 43 importing, into Gephi 44 data laboratory about 23, 29 CSV import 24 Excel import 25 Graph file import 25 helper 30 manual entry 24 MySQL import 25 data sources about 345 identifying 43 degree centrality 16, 193, 201 degree centrality (undirected graphs) 186 Degree Range filter 155 density 19 diameter, network measures 182 diffusion about 119-122 network, viewing 136-139 Directed Acyclic Graphs (DAG) layout 78, 94 DL files 282 DNA about 243 topology-based DNA 245 types 244 uses 244 Dual Circle layout 80, 81, 94 dynamic attribute networks implementing 264-273 viewing 264-273 [ 350 ] dynamic filtering 152 dynamic GEXF files creating 274-277 URL 274 Dynamic Network Analysis See  DNA dynamic network, topology-based DNA generating 245, 246 implementing 251 viewing 251 E eccentricity, network measures 183 eccentricity, network statistics 200 edge betweenness, network statistics 200, 201 edge betweenness statistic, network measures 185 Edge Cut 75 edge pencil tool 49 edges about 153 Edge Weight filter 153 filtering 160-162 Self-Loop filter 153 Ego Network function 155 eigenvector centrality 15, 187, 195, 203, 204 embeddedness, clustering statistics 197 Equal filter regex function, applying 159, 160 using 157-159 Equal function 152 Erdos number, network measures about 184 URL 321 Excel import, data laboratory 25 expansion 221 exports about 30 ExportToEarth 31 Graph Streaming plugin 30 Seadragon Web Export 30 Sigmajs Exporter 30 F filtering about 150 complex filters 168 Equal filter 157, 158 functions 151, 152 primary filtering 151 simple filters 156 tab 27 Force Atlas layout 73, 74, 94 Force Atlas 3D layout 74 Force Atlas layout 56, 57, 72, 73, 94 force-based layouts attraction 70 gravity 71 repulsion 71 friends of friends' network 166 Fruchterman-Reingold algorithm 74, 94 Fruchterman-Reingold layout 58 G GDF files 282, 283 generators about 31-33 using 126-131 geodesic path 11 geographic layouts about 83 Geo layout 83 Maps of Countries layout 84 Geo layout 83, 94 Gephi about 7, existing networks 316-323 overview 22 projects, creating 324, 325 toolkit, URL wiki, URL 316, 345 Giant Component filter 155 Give color to nodes (Tools) 36 [ 351 ] graph analyzing 48, 62-64 applications collaboration exporting 68 information linkages modifying 49, 65-67 natural-world networks technological networks Who-Talks-to-Whom graphs graph aesthetics about 107 example 108-111 graph density, network measures 183 graph density, network statistics 200 Graph Exchange XML Format (GEXF) files about 283 URL 283 graph file exporters about 280, 281 CSV files 281 DL files 282 GDF files 282, 283 Graph Exchange XML Format (GEXF) files 283 GraphML files 284, 285 Graph Modeling Language (GML) files 284 NET files 285 VNA files 285, 286 graphing needs, Miles Davis network example about 87-89 analysis goal 89 dataset parameters 89, 90 interactivity 92, 93 network behaviors 91 network density 91 network display 91 temporal elements 92 GraphML files about 284 URL 285 Graph Modeling Language (GML) 284 graph statistics about 182 centrality measures 185, 186 centrality statistics, interpreting 193 clustering and neighborhood measures 188 clustering statistics, interpreting 196 interpreting 190 network measures 182 network measures, interpreting 190-193 used, for filtering 207-215 graph window 25 GUESS URL 282, 283 H hairball effect 34 Hiveplot layout 34, 82, 95 homophily about 19, 123, 124 identifying 144-147 Hyperlink-Induced Topic Search (HITS) function, network measures 184 I image exporters about 280, 286 PNG export 286, 287 SVG export 288 Image Preview tool URL 292 import processes 346 in-degree centrality 194 in-degree centrality (directed graphs) 187 In Degree Range filter 155 inflation 221 initial graph layout viewing 45, 46 Inkscape PDF file, editing 293, 294 Inter Edges 152 INTERSECTION filter 175, 177 INTERSECTION operator 154 intervals, topology-based DNA creating, in existing project 251-253 Intra Edges 152 Isometric layout 84, 95 [ 352 ] K M KONECT URL 345 Maps of Countries layout 84, 95 Markov Clustering plugin about 221 using 237-241 Marvel Social network 316 MASK (Edges) operator 154 modularity, clustering and neighborhood measures 189 modularity statistic, clustering statistics 197 Multipartite layout 34, 85, 95 MySQL import, data laboratory 25 L Layered layout 35, 85, 95 layout about 34 additional 84 additional layout tools 86 ARF layout 36, 71, 72 ARF layout, testing 97-100 Circular layout 35, 78, 79 Concentric layout 35, 101, 102 Force Atlas layout 73, 74 Force Atlas 3D layout 74 Force Atlas layout 72, 73 Force-based layouts 70, 71 Fruchterman-Reingold algorithm 75 geographic layouts 83 Hiveplot layout 34 Isometric layout 85 Layered layout 35, 85 Multipartite layout 34, 85 Network Splitter 3D 86 OpenOrd algorithm 75 OpenOrd layout 35 Radial Axis layout, testing 103-105 radial layouts 81 selecting 47 selection, criteria 106 strengths 93-96 testing 97 tree layouts 77, 78 types 69, 70 weaknesses 93-96 Yifan Hu algorithm 76 Yifan Hu multilevel approach 77 Yifan Hu Proportional layout 76 layouts tab 28 Link Communities, clustering and neighborhood measures 189 Link Communities statistic, clustering statistics 197 links 10 Loxa Web Site Export 298, 300, 308-313 N neighborhood overlap and embeddedness, clustering and neighborhood measures 189 Neighbors Network filter 155 NET files 285 NetLogo URL 346 network clustering 140-144 contagion 20 contagion network, viewing 131-135 diffusion 20 diffusion, viewing 136-139 growth 21, 22 growth, patterns 125, 126 network analysis future 341, 342 network diameter, network statistics 200 network graph analysis primer 10 analyzing 48 connectivity 13 cycles 12 data, formatting for Gephi 43 data, importing into Gephi 44 data sources, identifying 43 example graph, creating 51 exporting 50, 51 final output, determining 42 idea or topic, identifying 40, 41 initial graph layout, viewing 45, 46 layout, selecting 47 [ 353 ] modifying 49 paths 11 paths and connectivity 11 process flow, proposed 40 structure 13 network graph, example graph appropriate layout, selecting 56 data, formatting for Gephi 52-54 data, importing 54 data source, finding 52 Force Atlas layout 56, 57 Fruchterman-Reingold layout 58 initial network, viewing 55 Radial Axis layout 59 topic, identifying 51, 52 Yifan Hu layout 60 network graph, structure about 13, 14 behaviors 20 centrality 14-17 clustering 19 components 17, 18 density 19 giant component 18 homophily 19 network measures, graph statistics about 182 average path length 183 connected components 184 diameter 182 eccentricity 183 edge betweenness 185 Erdos number 184 graph density 183 Hyperlink-Induced Topic Search (HITS) function 184 network measures, interpreting about 190-192 average path length 192 diameter 191 eccentricity 191 graph density statistic 192 HITS 192 Network Splitter 3D layout 86, 96 network statistics about 199 average path length 200 centrality statistics 201 eccentricity 200 edge betweenness 200 graph density 200 network diameter 200 node pencil tool 49 Non-null condition 153 NOT (Edges) operator 154 NOT (Nodes) operator 154 number of triangles, clustering and neighborhood measures 189 number of triangles, clustering statistics 197 Num Iterations 76 Num Threads 76 O OpenOrd algorithm 75 OpenOrd layout 35, 96 operator about 154 INTERSECTION operator 154 MASK (Edges) operator 154 NOT (Edges) operator 154 NOT (Nodes) operator 154 UNION operator 154 out-degree centrality 194 out-degree centrality (directed graphs) 187 Out Degree Range filter 155 P painter function 49 Pajek program URL 285 Partition Count filter 153 Partition filter about 153 using 162, 163 partitioning 222-227 partitioning and clustering, examples about 222-227 [ 354 ] Chinese Whispers plugin, using 233-237 color and size options, using 228-230 manual graph segmentation 231-233 Markov Clustering plugin, using 237-241 Ranking tab 228 partitioning and clustering, options about 218, 219 Chinese Whispers algorithm 221 manual settings 220 Markov clustering 221 Partition tab 219 Ranking tab 220 Partition tab 219 paths 11 PDF export about 292 PDF file, editing in Inkscape 293, 294 plugins about 28, 29 additional 36 clustering 29 data laboratory 29 data laboratory, helper 30 exports 30 generators 31-33 Give color to nodes (tools) 36 layout 34 Link Communities (metrics) 36 PNG export 286, 287 preview window about 26 PDF option 26 PNG option 26 SVG option 26 primary filtering about 151 attributes 151-153 edges 151-153 operator 151-154 topology 151-155 primary windows about 23 data laboratory 23 graph window 25 preview window 26 project, high school network about 333 creating, as PDF 338-340 network in Gephi, exploring 334-338 project, Newman NetScience dataset about 325 network in Gephi, exploring 326-330 project, deploying to Web 331, 332 R Radial Axis layout about 59, 82, 83, 96 testing 103-105 radial layout about 81 Hiveplot layout 82 Radial Axis layout 82, 83 Range filter 153 Ranking tab about 220 working with 228 RDF URL 152 Regular Expression (regex) about 318 applying 159, 160 repulsion strength 72 S Scalable Vector Graphics See  SVG export Seadragon 300, 302 Seadragon Web Export 30, 294, 295 secondary windows (tabs) about 27 filtering tab 27 layouts tab 28 statistics tab 27, 28 Self-Loop filter 153 semantic filtering 152 Sigma.js Exporter 30, 296-308 simple filters about 156, 157 edges, filtering 160-162 [ 355 ] Equal filter, using 157-159 Partition filter, using 162, 163 regex function, applying 159, 160 topology filters 164-167 SIR model (Susceptible, Infectious, and Removed) 117 SIRS model 118 SIS model 118 six degrees of separation 63 sizer function 49 small world networks 129 Social Network Analysis (SNA) Stanford Large Network Dataset Collection URL 43 Stanford Network Analysis Project (SNAP) URL 345 statistical measures, application about 198 basic statistical applications 198 graph statistics, used, for filtering 207-215 statistics tab 27, 28 subfilters, complex filters using 169-174 SVG export about 288 SVG file, editing with Inkscape 288-292 T ties 10 time intervals, topology-based DNA about 246, 247 adding, to new project 254 timelines, topology-based DNA about 248, 249 applying 259, 260 as filters 260, 261 working with 258 topology Degree Range filter 155 Ego Network function 155 Giant Component filter 155 In Degree Range filter 155 Neighbors Network filter 155 Out Degree Range filter 155 topology-based DNA about 245 data, importing 249, 250 data, preparing 249, 250 dynamic network, generating 245, 246 dynamic network, implementing 251 dynamic network, viewing 251 existing GEXF file, using 254, 255 multiple timeframes, adding 255-257 time intervals 246, 247 time intervals, adding to new project 254 time intervals, creating in existing project 251-253 timelines 248, 249 timelines, applying 259, 260 timelines, as filters 260, 261 timelines, working with 258 topology filters, simple filters 164-167 topology, primary filtering 154, 155 tree layouts about 77, 78 Directed Acyclic Graphs (DAG) layout 78 U UCINET program URL 282 UNION operator 154, 178, 179 V VNA files 285, 286 W Watts-Strogatz small world Alpha model 130 web exporters about 280, 294 Loxa Web Site Export 294, 298-300 Seadragon Web Export 294, 295 Sigma.js Exporter 294, 296-298 web graph exporting 300 Loxa Web Site Export 308-313 Seadragon 300-302 Sigma.js Exporter 302- 308 [ 356 ] web sources 346 Who-talks-to-Whom graphs Y Yifan Hu algorithm 76, 96 Yifan Hu layout 60 Yifan Hu multilevel approach 77, 96 Yifan Hu Proportional layout 76, 96 [ 357 ] Thank you for buying Mastering Gephi Network Visualization About Packt Publishing Packt, pronounced 'packed', published its first book, Mastering phpMyAdmin for Effective MySQL Management, in April 2004, and subsequently continued to specialize in publishing highly focused books on specific technologies and solutions Our books and publications share the experiences of your fellow IT professionals in adapting and customizing today's systems, applications, and frameworks Our solution-based books give you the knowledge and power to customize the software and technologies you're using to get the job done Packt books are more specific and less general than the IT books you have seen in the past Our unique business model allows us to bring you more 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idea is still at an early stage and you would like to discuss it first before writing a formal book proposal, then please contact us; one of our commissioning editors will get in touch with you We're not just looking for published authors; if you have strong technical skills but no writing experience, our experienced editors can help you develop a writing career, or simply get some additional reward for your expertise Network Graph Analysis and Visualization with Gephi ISBN: 978-1-78328-013-1 Paperback: 116 pages Visualize and analyze your data swiftly using dynamic network graphs built with Gephi Use your own data to create network graphs displaying complex relationships between several types of data elements Learn about nodes and edges, and customize your graphs using size, color, and weight attributes Filter your graphs to focus on the key information you need to see and publish your network graphs to the Web Google Visualization API Essentials ISBN: 978-1-84969-436-0 Paperback: 252 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Using Gephi to understand existing networks Creating new Gephi projects Project – Newman NetScience dataset Exploring the network in Gephi Deploying the project to the Web Project – high school network. .. using Gephi What this book covers Chapter 1, Fundamentals of Complex Networks and Gephi, provides background into the world of complex networks and how we can use Gephi to explore and analyze network