gephi icwsm tutorial 110717064641 phpapp02

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ICWSM11 TutorialExploratory Network Analysis with:

Instructors: Sbastien Heymann, Julian [email protected],[email protected]

July 17, 2011 | 1 PM - 4 PM
mailto:seb%40gephi.org?subject=mailto:julian.bilcke%40gephi.org?subject=mailto:julian.bilcke%40gephi.org?subject=mailto:seb%40gephi.org?subject=


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Exploratory Network Analysis with Gephi

This tutorial is an introduction to Gephi, the open source graph networkvisualization and manipulation software.

Gephi aims to fulll the complete chain from data importing to aesthetics

renements and interaction.

Users interact with the visualization and manipulate structures, shapesand colors to reveal hidden properties.

The goal is to help data analysts to make hypotheses, intuitively discoverpatterns or errors in large data collections.

At the end, the participants will walk away with the practical knowledgeenabling them to use Gephi for their own projects.

OFFLI

NE


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Exploratory Network Analysis with Gephi

It starts with a brief introduction on the network exploration process anda 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 renements.

Next, teams work on real datasets.

They nally present their preliminary results. The tutorial concludes with

a general question and answer session.

OFFLI

NE


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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/
http://gephi.org/users/supported-graph-formats/http://gephi.org/users/supported-graph-formats/


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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 *


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Workshop Schedule - Part II

Hands-On!

Team Work on a Dataset Presentation of Preliminary Results

Q&A


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Exploratory Data Analysis

The greatest value of a picture is when it forces usto notice what we never expected to see

started withJohn Tukey (1962)

Conrmatory

Exploratory

Serendipity

resultsintuition

surprise


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Exploratory Data Analysis

Non-linear processing chain of Ben Fryin Computational Information Design (2004)


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Dummy Example

P2P le size distribution (Latapy et al., 2008)

Observation:visual saliences on specic

le sizes

External knowledge:these sizes correspond tolms

New hypothesis on data:lms are highly exchanged,

so the study might dig in

this direction


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Exploratory Network Analysis

see the network11st graph viz tool: Pajek (1996)

Vladimir Batagelj, Andrej Mrvar

interact in real time2

3

Gephi prototype (2008)

group, lter, compute metrics...

size by rank, color by partition,label, curved edges, thickness...

build a visual language


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Looking for a Simple Small Truth?

Drew Conway, What Data Visualization Should Do: 1. Make complex things simple2. Extract small information from large data3. Present truth, do not deceive

http://www.dataists.com/2010/10/what-data-visualization-should-do-simple-small-truth/
http://www.dataists.com/2010/10/what-data-visualization-should-do-simple-small-truth/http://www.dataists.com/2010/10/what-data-visualization-should-do-simple-small-truth/


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Looking for Orderness in Data

Make varying 3 cursorssimultaneously to extractmeaningful patterns

MICRO level MACRO level

1 dimension N dimensions

T+0 T+N

at different levels

on multiple dimensions

at time scale


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Zoom cursor on Quantitative Data

Global

- connectivity- density- centralization

Local

- communities- bridges between communities- local centers vs periphery

Individual- centrality- distances- neighborhood- location- local authority vs hub

MICRO level MACRO level


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Crossing cursor on Qualitative Data

Social

- who with whom- communities

- brokerage- inuence and power

- homophily

Semantic

- topics

- thematic clusters

Geographic

- spatial phenomena

1 dimension N dimensions


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Timeline cursor on Temporal Data

Evolution of social ties

Evolution of communities

Evolution of topics

T+0 T+N


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Mapping an Innovation CenterCollaborations on projects at Images et Rseaux

Themes and content

Actors

Territory

Franck Ghitalla & Ecole de Design de Nantes


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Mapping Scientic Cooperations


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Network Map: a Series of Choices

corpus

data

algorithms

thresholds

graphical

operations

communication

goals


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Guideline

lists + edges in bonus, focus on qualitative data

How attributes explain the structure? 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 entitieschallenge: mix attribute crossing and connectivity

How the structure explains attributes? hard to read, problem of hidden signals:

track patterns with various layouts and ltering focus on structures metrics are tools to build the graph (cosine similarity...) categories help to understand the structurechallenge: pattern recognition

require high computational power

1 - 100

100 - 1,000

1,000 - 50,000

> 50,000

# nodes


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Gephi in a Nutshell

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


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Gephi Community

ContributorsCommunities

Mathieu Bastian, Mathieu Jacomy,Eduardo Ramos Ibaez, SbastienHeymann, Guillaume Ceccarelli,Andr Panisson, Antonio Patriarca,Cezary Bartosiak, Martin kurla,Patrick McSweeney, Yi Du, HlderSuzuki, Daniel Bernardes, ErnestoAneiro, Keheliya Gallaba, LuizRibeiro, Urban kudnik, VojtechBardiovsky, Yudi Xue

Nonprot organization


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Community Mission

Provide a sustainable software

Maintain the technical ecosystem

Build a business ecosystem

Face cutting-edge technological challenges witha long-term vision

Distribute the software in Open Source


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Community Values

Open innovation: ideas and features come fromthe entire community.

Decisions are taken with transparency.

We consider this technology as a public good,and will keep it in open source.


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Diversity of Usages

business leisure :-)

communication academic art


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Diversity of Network Encoding

V = { a, b, c, d, e }E = { (a,b), (a,d), (b,c), (e,a), (c,e) }

Textual

a b c d ea - 1 - 1 -b - - 1 - -c - - - - 1d - - - - -e 1 - - - -

Tabular

XMLGraphical

and many others...


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Software I/O

} >graph streaming

databases

le

le

CSVPajek NETGuess GDFGEXFGraphMLGraphviz DOTUCInet DLNetdrawVNA

Tulip TLPExcel Spreadsheet

MySQLPostgreSL

SQL ServerNeo4j

CSV

Pajek NETGuess GDFGEXFGraphMLExcel SpreadsheetSVGPDFPNG

user input


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Choosing a File Format

Table of features supportedby Gephi

* spreadsheets can be loaded

in the Data Laboratory

Ed

geList/M

atrix

Stru

cture

XMLS

truture

Edge

Weig

ht

Attribu

tes

Visualizatio

nAttribu

tes

Attribu

teDefaultVa

lue

Hierarchica

lGraph

s

Dyna

mics

CSV

DL Ucinet

DOT Graphviz

GDF

GEXF

GML

GraphML

NET Pajek

TLP Tulip

VNA Netdraw

Spreadsheet*


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Do you need...

GEXFSpreadsheetGraphMLGuess GDF

GMLUCINet DLNetdraw VNAGraphviz DOTPajek NETCSV

Tulip TLP

Many features

Few features

XML

TabularText

File Type


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Using Gephi


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Team work

Create a team of 2~3 people.1

Two teams present their preliminary ndings.

Explore it during 1H.

Choose a dataset.2

3

4


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Dataset #1: GitHub Software Repository

GitHub is an application used by nearly a million people to storeover 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 andmake the code easier to contribute.

https://github.com
https://github.com/https://github.com/


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Data extracted by Franck Cuny* at Linkuence SAS

1st release in March 2010 -> this poster2nd release in June 2011 -> your data

_____________Network of user proles__________Nodes: peoples with at least one repository whoare followed by at least two other peopleEdges: A follows B

_____________Network of repositories__________

Nodes: repositoriesEdges: A shares a developer with B

Very few research publications on this OSN!

* [email protected]
mailto:franck.cuny%40linkfluence.net?subject=Github%20datasetmailto:franck.cuny%40linkfluence.net?subject=Github%20dataset


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Data extracted by a crawl using the GitHub APISeed: 10 well-known contributors in the Perl community

Networks by country: Japan, France, United StatesNetworks 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


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Your mission (should you decide to accept it):nd research hypotheses based on your exploration

Example question: are the Perl communities based on geography?


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Dataset #2: The Irish Blogosphere

_______________Blogroll Network______________

Nodes: blogs with more than two blogroll linksEdges: blogroll link (in-link)

_______________Post-link Network_____________

Nodes: blogs with more than two blogroll linksEdges: hyperlink inside post from a blog to another(post-link)

Identifying Representative Textual Sources in Blog Networks. K. Wade, D.Greene, C. Lee, D. Archambault, P. Cunningham (2011) http://mlg.ucd.ie/blogs
http://mlg.ucd.ie/blogshttp://mlg.ucd.ie/blogs


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Data extracted by a crawl at distance 2 from the seed for the in-linksand 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


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Your mission:explore and try to conrm the ofcial results


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Hands-On!

Start:

Load a graph Apply a 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, lter the network

End:

Export maps to PDF in Preview Tab

Save


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Presentations

GitHub Repository Irish Blogosphere


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Gephi Documentation

Web Site:

Support:Wiki:Source code:

Online Tutorialshttp://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_Datahttp://wiki.gephi.org/index.php/Import_Dynamic_Data

Tutorial in Spanishhttps://code.google.com/p/camon/wiki/Taller_Gephi

Supported Graph Formatshttp://gephi.org/users/supported-graph-formats/

http://gephi.org

http://forum.gephi.org

http://wiki.gephi.org

https://launchpad.net/gephi
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_Datahttp://wiki.gephi.org/index.php/Import_Dynamic_Datahttps://code.google.com/p/camon/wiki/Taller_Gephihttp://gephi.org/users/supported-graph-formats/http://gephi.org/http://forum.gephi.org/http://wiki.gephi.org/https://launchpad.net/gephihttps://launchpad.net/gephihttp://wiki.gephi.org/http://forum.gephi.org/http://gephi.org/http://gephi.org/users/supported-graph-formats/https://code.google.com/p/camon/wiki/Taller_Gephihttp://wiki.gephi.org/index.php/Import_Dynamic_Datahttp://wiki.gephi.org/index.php/Import_CSV_Datahttp://gephi.org/users/tutorial-layouts/http://gephi.org/users/tutorial-visualization/http://gephi.org/users/quick-start/


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Thank You!

Caspar David Friedrich -

Wanderer Above the Sea of Fog


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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] Benot 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 Tsanghttp://jeunhotsang.com/blog/2010/12/07/prototype/

[slide 27] sketches from Ben Fry, Computational Information Design

Special Thanks to Franck Ghitalla and Mathieu Jacomyfor their insightful discussions.
http://www.dataists.com/2010/10/what-data-visualization-should-do-simple-small-truth/http://jeunhotsang.com/blog/2010/12/07/prototype/http://jeunhotsang.com/blog/2010/12/07/prototype/http://www.dataists.com/2010/10/what-data-visualization-should-do-simple-small-truth/

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