Download as PDF, PPTX
![Requirements
Bring your own laptop with Java and Gephi installed.
Gephi should be updated (menu Help > Check for Updates).
Bring a mouse with a wheel.
Bring a dataset of your own if you want, verify if it loads well in Gephi.[1]
[1] http://gephi.org/users/supported-graph-formats/](https://image.slidesharecdn.com/gephi-icwsm-tutorial-121007223443-phpapp02/85/Gephi-icwsm-tutorial-4-320.jpg)
![Credits
[slide 11] images from Drew Conway
http://www.dataists.com/2010/10/what-data-visualization-should-do-simple-small-truth/
[slide 22 top left] Benoît Vidal at MFG Labs
[slide 22 bottom center] Franck Ghitalla at UTC
[slide 22 right] Studies in MA Digital Fashion at LCF by Peter Jeun Ho Tsang
http://jeunhotsang.com/blog/2010/12/07/prototype/
[slide 27] sketches from Ben Fry, Computational Information Design
Special Thanks to Franck Ghitalla and Mathieu Jacomy
for their insightful discussions.](https://image.slidesharecdn.com/gephi-icwsm-tutorial-121007223443-phpapp02/85/Gephi-icwsm-tutorial-43-320.jpg)
![Requirements
Bring your own laptop with Java and Gephi installed.
Gephi should be updated (menu Help > Check for Updates).
Bring a mouse with a wheel.
Bring a dataset of your own if you want, verify if it loads well in Gephi.[1]
[1] http://gephi.org/users/supported-graph-formats/](https://image.slidesharecdn.com/gephi-icwsm-tutorial-121007223443-phpapp02/75/Gephi-icwsm-tutorial-4-2048.jpg)
![Credits
[slide 11] images from Drew Conway
http://www.dataists.com/2010/10/what-data-visualization-should-do-simple-small-truth/
[slide 22 top left] Benoît Vidal at MFG Labs
[slide 22 bottom center] Franck Ghitalla at UTC
[slide 22 right] Studies in MA Digital Fashion at LCF by Peter Jeun Ho Tsang
http://jeunhotsang.com/blog/2010/12/07/prototype/
[slide 27] sketches from Ben Fry, Computational Information Design
Special Thanks to Franck Ghitalla and Mathieu Jacomy
for their insightful discussions.](https://image.slidesharecdn.com/gephi-icwsm-tutorial-121007223443-phpapp02/75/Gephi-icwsm-tutorial-43-2048.jpg)
![SSII2021 [SS2] Deepfake Generation and Detection – An Overview （ディープフェイクの生成と検出）](https://cdn.slidesharecdn.com/ss_thumbnails/ss2-01-210607043612-thumbnail.jpg?width=600ounds&width=560&fit=bounds)
Gephi icwsm-tutorial
- 1. ICWSM’11 Tutorial Exploratory NetworkAnalysis with: Instructors: Sébastien Heymann, Julian Bilcke seb@gephi.org, julian.bilcke@gephi.org July 17, 2011 | 1 PM - 4 PM
- 2. Exploratory Network Analysiswith Gephi This tutorial is an introduction to Gephi, the open source graph network visualization and manipulation software. Gephi aims to fulfill the complete chain from data importing to aesthetics refinements and interaction. Users interact with the visualization and manipulate structures, shapes and colors to reveal hidden properties. The goal is to help data analysts to make hypotheses, intuitively discover patterns or errors in large data collections. E At the end, the participants will walk away with the practical knowledge IN enabling them to use Gephi for their own projects. F F L O
- 3. Exploratory Network Analysiswith Gephi It starts with a brief introduction on the network exploration process and a hands-on demonstration of the essential functionalities of Gephi. Participants are guided step by step through the complete chain of rep- resentation, manipulation, layout, analysis and aesthetics refinements. Next, teams work on real datasets. They finally present their preliminary results. The tutorial concludes with a general question and answer session. IN E F F L O
- 4. Requirements Bring your ownlaptop with Java and Gephi installed. Gephi should be updated (menu Help > Check for Updates). Bring a mouse with a wheel. Bring a dataset of your own if you want, verify if it loads well in Gephi.[1] [1] http://gephi.org/users/supported-graph-formats/
- 5. Workshop Schedule -Part I Exploratory Network Analysis • Exploratory Data Analysis • Exploratory Network Analysis • Looking for Orderness in Data • Examples • Guideline Introduction to Gephi • Approach and Community • Networked Data • Quick Start Demo * 30 min break *
- 6. Workshop Schedule -Part II Hands-On! • Team Work on a Dataset • Presentation of Preliminary Results Q&A
- 7. Exploratory Data Analysis Confirmatory results Exploratory intuition Serendipity surprise “The greatest value of a picture is when it forces us started with to notice what we never expected to see” John Tukey (1962)
- 8. Exploratory Data Analysis Non-linear processing chain of Ben Fry in Computational Information Design (2004)
- 9. Dummy Example Observation: visual saliences on specific file sizes External knowledge: these sizes correspond to films New hypothesis on data: films are highly exchanged, so the study might dig in this direction P2P file size distribution (Latapy et al., 2008)
- 10. Exploratory Network Analysis 2 interact in real time 1 see the network Gephi prototype (2008) 1st graph viz tool: Pajek (1996) group, filter, compute metrics... Vladimir Batagelj, Andrej Mrvar 3 build a visual language size by rank, color by partition, label, curved edges, thickness...
- 11. Looking for a“Simple Small Truth”? Drew Conway, What Data Visualization Should Do: 1. Make complex things simple 2. Extract small information from large data 3. Present truth, do not deceive http://www.dataists.com/2010/10/what-data-visualization-should-do-simple-small-truth/
- 12. Looking for Ordernessin Data Make varying 3 cursors simultaneously to extract meaningful patterns MICRO level MACRO level at different levels 1 dimension N dimensions on multiple dimensions T+0 T+N at time scale
- 13. “Zoom” cursor onQuantitative Data MICRO level MACRO level Global - connectivity - density - centralization Local - communities - bridges between communities - local centers vs periphery Individual - centrality - distances - neighborhood - location - local authority vs hub
- 14. “Crossing” cursor onQualitative Data 1 dimension N dimensions Social - who with whom - communities - brokerage - influence and power - homophily Semantic - topics - thematic clusters Geographic - spatial phenomena
- 15. “Timeline” cursor onTemporal Data T+0 T+N Evolution of social ties Evolution of communities Evolution of topics
- 16. Mapping an InnovationCenter Collaborations on projects at Images et Réseaux Themes and content Actors Territory Franck Ghitalla & Ecole de Design de Nantes
- 17.
- 18. Network Map: aSeries of Choices corpus data graphical operations algorithms communication thresholds goals
- 19. Guideline # nodes 1 - 100 lists + edges in bonus, focus on qualitative data How attributes explain the structure? 100 - 1,000 • easy to read, “obvious” patterns • focus on entities (in context) • metrics are tools to describe the graph (centrality, bridging...) • links help to build and interpret categories of entities challenge: mix attribute crossing and connectivity How the structure explains attributes? 1,000 - 50,000 • hard to read, problem of “hidden signals”: track patterns with various layouts and filtering • focus on structures • metrics are tools to build the graph (cosine similarity...) • categories help to understand the structure challenge: pattern recognition > 50,000 require high computational power
- 20.
- 21. Gephi in aNutshell « Like Photoshop™ for graphs. » Helps data analysts to reveal patterns and trends, highlight outliers and tells story with their data. • Network visualization platform • Open source, supported by a community • Built for performance and usability • Extensible by plug-ins • Windows, MacOS X, Linux
- 22. Gephi Community Nonprofit organization Communities Contributors Mathieu Bastian, Mathieu Jacomy, Eduardo Ramos Ibañez, Sébastien Heymann, Guillaume Ceccarelli, André Panisson, Antonio Patriarca, Cezary Bartosiak, Martin Škurla, Patrick McSweeney, Yi Du, Hélder Suzuki, Daniel Bernardes, Ernesto Aneiro, Keheliya Gallaba, Luiz Ribeiro, Urban Škudnik, Vojtech Bardiovsky, Yudi Xue
- 23. Community Mission Provide a “sustainable” software Maintain the technical ecosystem Build a business ecosystem Face cutting-edge technological challenges with a long-term vision Distribute the software in Open Source
- 24. Community Values Open innovation: ideas and features come from the entire community. Decisions are taken with transparency. We consider this technology as a public good, and will keep it in open source.
- 25. Diversity of Usages business leisure :-) communication academic art
- 26. Diversity of NetworkEncoding V = { a, b, c, d, e } <graph> E = { (a,b), (a,d), (b,c), (e,a), (c,e) } <nodes> <node id=”a” /> <node id=”b” /> Textual <node id=”c” /> <node id=”d” /> <node id=”e” /> </nodes> <edges> <edge source=”a” target=”b” /> <edge source=”a” target=”d” /> a b c d e <edge source=”b” target=”c” /> a - 1 - 1 - <edge source=”e” target=”a” /> <edge source=”c” target=”e” /> b - - 1 - - </edges> c - - - - 1 </graph> d - - - - - e 1 - - - - XML Graphical Tabular and many others...
- 27. Software I/O } MySQL PostgreSL SQL Server databases user input Neo4j CSV CSV Pajek NET Pajek NET file Guess GDF Guess GDF > GEXF GEXF GraphML GraphML file Graphviz DOT Excel Spreadsheet UCInet DL SVG NetdrawVNA PDF Tulip TLP PNG Excel Spreadsheet graph streaming
- 28. Choosing a FileFormat re es e tu lu ut c Va ru s rib ph St lt t ra At au rix G re ef n t at gh al io tu D /M ic es s at ei ru ic e h st ut liz W ut am rc St Li rib rib ua ra ge L yn ge ie XM s t t Ed At At Vi D H Ed CSV Table of features supported DL Ucinet by Gephi DOT Graphviz GDF GEXF * spreadsheets can be loaded GML in the Data Laboratory GraphML NET Pajek TLP Tulip VNA Netdraw Spreadsheet*
- 29. Do you need... Many features GEXF Spreadsheet GraphML Guess GDF GML UCINet DL Netdraw VNA Graphviz DOT Pajek NET File Type CSV XML Tulip TLP Tabular Few features Text
- 30. Using Gephi E M O D
- 31. Team work 1 Create a team of 2~3 people. 2 Choose a dataset. 3 Explore it during 1H. 4 Two teams present their preliminary findings.
- 32. Dataset #1: GitHubSoftware Repository “GitHub is an application used by nearly a million people to store over two million code repositories, making GitHub the largest code host in the world.” Started in 2008, it provides the features of an online social network and a software repository to lower the barriers of collaboration and make the code easier to contribute. https://github.com
- 33. Dataset #1: GitHubSoftware Repository Data extracted by Franck Cuny* at Linkfluence SAS 1st release in March 2010 -> this poster 2nd release in June 2011 -> your data _____________Network of user profiles__________ Nodes: peoples with at least one repository who are followed by at least two other people Edges: A follows B _____________Network of repositories__________ Nodes: repositories Edges: A shares a developer with B Very few research publications on this OSN! * franck.cuny@linkfluence.net
- 34. Dataset #1: GitHubSoftware Repository Data extracted by a crawl using the GitHub API Seed: 10 well-known contributors in the Perl community Networks by country: Japan, France, United States Networks by language: Perl, PHP, Python, Ruby Node attributes: • user country • number of followers • main programming language Edges: • directed • weight = number of projects A has forked from B
- 35. Dataset #1: GitHubSoftware Repository Your mission (should you decide to accept it): find research hypotheses based on your exploration Example question: are the Perl communities based on geography?
- 36. Dataset #2: TheIrish Blogosphere “Identifying Representative Textual Sources in Blog Networks”. K. Wade, D. Greene, C. Lee, D. Archambault, P. Cunningham (2011) http://mlg.ucd.ie/blogs _______________Blogroll Network______________ Nodes: blogs with more than two blogroll links Edges: blogroll link (in-link) _______________Post-link Network_____________ Nodes: blogs with more than two blogroll links Edges: hyperlink inside post from a blog to another (post-link)
- 37. Dataset #2: TheIrish Blogosphere Data extracted by a crawl at distance 2 from the seed for the in-links and Google Blog Search for the post-links. Seed: 21 popular blogs, winners of the “2010 Irish Blog Awards” Node attributes: • post count = total number of posts by blog • category = from the irish blog index at www.irishblogdirectory.com, where available • infomap_comm = community to which a node belongs (infomap algo) • gce_comms = overlapping communities (GCE algo) • moses_comms = overlapping communities (MOSES algo) Edges: • directed • weight = number of hyperlinks in the Post-link network crawl at distance 2 from the seed
- 38. Dataset #2: TheIrish Blogosphere Your mission: explore and try to confirm the official results
- 39. Hands-On! Start: • Load a graph • Applya layout • Color the nodes by a qualitative variable in Partition Panel • Size the nodes by a quantitative variable in Ranking Panel • Start to explore...compute metrics, filter the network End: • Export maps to PDF in Preview Tab • Save
- 40. Presentations GitHubRepository Irish Blogosphere
- 41. Gephi Documentation Web Site: http://gephi.org Support: http://forum.gephi.org Wiki: http://wiki.gephi.org Source code: https://launchpad.net/gephi Online Tutorials http://gephi.org/users/quick-start/ http://gephi.org/users/tutorial-visualization/ http://gephi.org/users/tutorial-layouts/ http://wiki.gephi.org/index.php/Import_CSV_Data http://wiki.gephi.org/index.php/Import_Dynamic_Data Tutorial in Spanish https://code.google.com/p/camon/wiki/Taller_Gephi Supported Graph Formats http://gephi.org/users/supported-graph-formats/
- 42. Thank You! Caspar David Friedrich - Wanderer Above the Sea of Fog
- 43. Credits [slide 11] imagesfrom Drew Conway http://www.dataists.com/2010/10/what-data-visualization-should-do-simple-small-truth/ [slide 22 top left] Benoît Vidal at MFG Labs [slide 22 bottom center] Franck Ghitalla at UTC [slide 22 right] Studies in MA Digital Fashion at LCF by Peter Jeun Ho Tsang http://jeunhotsang.com/blog/2010/12/07/prototype/ [slide 27] sketches from Ben Fry, Computational Information Design Special Thanks to Franck Ghitalla and Mathieu Jacomy for their insightful discussions.