awesome-network-analysis/README.md

Awesome Network Analysis

An awesome list of resources to construct, analyze and visualize network data.

Table of Contents

• Books
  • General Overviews
  • Language-specific
  • Method-specific
  • Topic-specific
• Conferences
• Courses
• Datasets
• Journals
• Professional groups
• Review Articles
• Software
  • JavaScript
  • Python
  • R
• Tutorials
• Varia
• Contribution Guidelines

Books

General Overviews

1. Networks. An Introduction, by Mark E.J. Newman (2010).

• Networks, Crowds, and Markets: Reasoning About a Highly Connected World, by David Easley and Jon Kleinberg; complete pre-publication draft online (2010).
• Réseaux sociaux et structures relationnelles, by Emmanuel Lazega, in French (2014).
• Sociologie des réseaux sociaux, by Pierre Mercklé, in French (2011).
• Social Network Analysis. Methods and Applications, by Stanley Wasserman and Katherine Faust (1994).

Method-specific

1. Exponential Random Graph Models for Social Networks, by Dean Lusher, Johan Koskinen and Garry Robins (2013).

• Multilevel Network Analysis for the Social Sciences, by Emmanuel Lazega and Tom A.B. Snijders (2016).
• Political Networks. The Structural Perspective, by David Knoke (1994).

Software-specific

1. Analyzing Social Networks (using UCINET), by Stephen P. Borgatti, Martin G. Everett and Jeffrey C. Johnson (2013).

• Network Analysis with R/igraph, by Gabor Csárdi, Thomas Nepusz and Eduardo M. Airoldi (in preparation).
• Network Analysis with Python/igraph, by Thomas Nepusz, Gabor Csárdi and Eduardo M. Airoldi (in preparation).
• Statistical Analysis of Network Data with R, by Eric D. Kolaczyk and Gabor Csárdi (2014).

Topic-specific

1. Neighbor Networks. Competitive Advantage Local and Personal, by Ronald S. Burt (2010).

• Comparing Policy Networks. Labor Politics in the U.S., Germany, and Japan, by David Knoke et al. (1996).

Conferences

1. European Conference on Social Networks (EUSN).

• PolNet - Annual Political Networks Workshops and Conference (APSA).
• NetSci (Network Science Society).
• Sunbelt (INSNA).

Courses

1. Complex Networks, by Peter Sheridan Dodds (University of Vermont, 2016).

• Networks (Economics), by Daron Acemoglu and Asu Ozdaglar (MIT, 2009).
• Social and Economic Networks: Models and Analysis, by Matthew O. Jackson (Stanford University via Coursera, 2015).
• Social Network Analysis, by Lara Adamic (University of Michigan via Coursera, not yet run).

Datasets

1. Bill Cosponsorship Networks in European Parliaments.

• Gephi Datasets.
• KONECT - The Koblenz Network Collection.
• James H. Fowler's Cosponsorship Network Data Page.
• Mark E.J. Newman's Network Data.
• Pajek Datasets
• Siena Datasets.
• Stanford Large Network Dataset Collection.
• tnet Datasets.
• UCI Network Data Repository.

Journals

1. Applied Network Science (Springer Open).

• Computational and Mathematical Organization Theory (Springer).
• Computational Social Networks (Springer Open).
• Connections (INSNA).
• Journal of Complex Networks (Oxford).
• The Journal of Mathematical Sociology (Taylor & Francis).
• Journal of Social Structure (INSNA).
• Network Science (Cambridge).
• REDES, in Spanish (INSNA).
• Social Network Analysis and Mining (Springer).
• Social Networks (Elsevier).

Professional groups

1. AFS RT 26 “Réseaux sociaux” - Thematic Network of the French Sociological Association, in French.

• ECPR Political Networks SG - Standing Group of the European Consortium for Political Research (Twitter account).
• GDR Analyse de réseaux en sciences humaines et sociales, in French.
• Groupe FMR - Flux, Matrices, Réseaux, in French.
• INSNA: International Network for Social Network Analysis (SOCNET mailing-list).
• NetSci: Network Science Society.
• APSA Political Networks - Organized Section of the American Political Science Association.

Review Articles

1. "Network Analysis and Political Science" (Annual Review of Political Science, 2011).

• "Statistical Models for Social Networks" (Annual Review of Sociology, 2011).

Software

1. Cytoscape - Cross-platform Java program to build, analyze and visualize networks.

• Discourse Network Analyzer (DNA) - Qualitative content analysis tool with network export facilities, written in Java with R integration.
• Gephi - Cross-platform, free and open source tool for network visualization.
• GraphViz - Software to draw graphs in the DOT language.
• Neo4j - Open source, scalable graph database, used by companies like Linkurious.
• networks.tb - A suite designed for analyzing socio-semantic networks, written in C.
• NodeXL - Free, open-source template for Microsoft Excel to explore network graphs.
• ORA-LITE - Windows program for dynamic meta-network assessment and analysis.
• Pajek - Windows program for large network analysis, free for noncommercial use.
• PNet - Simulation and estimation of exponential random graph models (ERGMs), written in Java for Windows.
• Siena - Simulation Investigation for Empirical Network Analysis, formerly a Windows program, now an R package.
• Stanford Network Analysis Project - C++ general purpose network analysis and graph mining library; available as a Python library and through NodeXL.
• UCINET - Windows commercial software package for the analysis of social network data.
• Visone - Cross-platform Java network analysis and visualization program, free for noncommercial use.
• VOSviewer - Cross-platform software tool for constructing and visualizing bibliometric networks, written in Java.

JavaScript

For more awesome Python packages, see the Awesome JavaScript list.

1. d3.js - JavaScript visualization library that can plot force-directed graphs.

• jLouvain - Louvain community detection for Javascript (example).
• Sigma - JavaScript library dedicated to graph drawing.
• vis.js - JavaScript library with network visualization capabilities.

Python

Most items below are from a Google spreadsheet by Michał Bojanowski and others.
For more awesome Python packages, see the Awesome Python list.

1. graphviz - Python renderer for the DOT graph drawing language.

• linkpred - Assess the likelihood of potential links in a future snapshot of a network.
• networkx - Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.
• python-igraph - Python version of the igraph network analysis package.

R

See also this Google spreadsheet by Ian McCulloh and others.
For more awesome R packages, see the Awesome R list.

1. Bergm - Tools to analyse Bayesian exponential random graph models (BERGM).

• CCAS - A statistical model for communication networks.
• concoR - A translation of the CONCOR network blockmodeling algorithm (blog post).
• ergm - Estimation of Exponential Random Graph Models (ERGM).
• GERGM - Estimation and diagnosis of the convergence of Generalized Exponential Random Graph Models (GERGM).
• geomnet - A single-geometry approach to network visualization with ggplot2.
• ggnetwork - A multiple-geometries approach to plot network objects with ggplot2.
• ggraph - A grammar of graph graphics built in the spirit of ggplot2.
• hergm - Estimate and simulate hierarchical exponential-family random graph models (HERGM) with local dependence.
• igraph - A collection of network analysis tools.
• influenceR - Compute various node centrality network measures by Burt, Borgatti and others.
• latentnet - Latent position and cluster models for network objects.
• networkD3 - D3 JavaScript network graphs from R.
• ndtv - Tools to construct animated visualizations of dynamic network data in various formats.
• network - Basic tools to manipulate relational data in R.
• networkDynamic - Support for dynamic, (inter)temporal networks.
• rgexf - Export network objects from R to GEXF, for manipulation with network software like Gephi or Sigma.
• RSiena - Simulation Investigation for Empirical Network Analysis; fits models to longitudinal network data.
• sna - Basic network measures and visualization tools.
• spnet - Methods for dealing with spatial social networks.
• statnet - The project behind many R network analysis packages.
• tergm - Fit, simulate and diagnose models for temporal exponential-family random graph models (TERGM).
• tnet - Network measures for weighted, two-mode and longitudinal networks.
• tsna - Tools for temporal social network analysis.
• visNetwork - Using vis.js library for network visualization.
• xergm - Extensions of Exponential Random Graph Models (ERGM, GERGM, TERGM, TNAM and REM).

Tutorials

1. Analyse des réseaux : une introduction à Pajek, in French (2011).

• Basic and Advanced Network Visualization with Gephi and R (2016).
• Exponential Random Graph Models (ERGMs) Using statnet (2015).
• Guides for Using the statnet Package (2010).
• Implementing an ERGM From Scratch in Python (2014).
• L'analyse des graphes bipartis, in French (2013).
• La détection de communautés avec Pajek 3.6, in French (2012).
• Modeling Valued Networks with statnet (2013).
• Network Analysis and Visualization with R and igraph (2016).
• Practical Social Network Analysis With Gephi (2014).
• Static and Dynamic Network Visualization with R (2015).
• Working with Bipartite/Affiliation Network Data in R (2012).

Varia

1. Blog Posts About Networks by Baptiste Coulmont, in French.

• Blog Posts About Networks by Pierre Mercklé, in French.
• Center for Network Science - A research center with a PhD in Network Science program at the Central European University in Budapest.
• Interdependence in Governance and Policy - Network-oriented research group led by Bruce Desmarais at Penn State University.
• Network Fact - Twitter account about networks, graph theory, and related topics.
• Social Network Analysis Software - Wikipedia English entry with links to many commercial and often outdated software.
• Videos from the Political Networks 2009 Conference.

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Contribution Guidelines

Please contribute to this list by sending a pull request after reading the Contribution Guidelines for stylistic indications.

• All sources are organized alphabetically with date and language mentions when relevant.
• Authors are mentioned for books and courses only.
• Journals require the name of the publisher.
• Review Articles require a journal and a date.
• R packages: please cite the stable CRAN version when it exists.
• Python packages: please cite the PyPi version when it exists.
• Software should be at least free for noncommercial use (only few exceptions will be granted).

Remember that an awesome list has to be, well, awesome. The "Awesome Manifesto" states:

Only awesome is awesome

Research if the stuff you're including is actually awesome. Put only stuff on the list you or another contributor can personally recommend and rather leave stuff out than include too much.

...

Comment on why something is awesome

Apart from suggesting a particular item on your list, you should also inform your readers why it's on the list and how they will benefit from it.

Please also add your name to the copyright waiver below, with an optional link to your personal homepage, GitHub profile or social media profile.

License

To the extent possible under law, the authors of this list (François Briatte, Ian McCulloh, Aditya Khanna) have waived all copyright and related or neighboring rights to this work.