awesome-network-analysis/README.md

Awesome Network Analysis

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

Table of Contents

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

Books

Overviews

• 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).

Method-specific

1. Multilevel Network Analysis for the Social Sciences, by Emmanuel Lazega and Tom A.B. Snijders (2016).

Software-specific

1. Analyzing Social Networks (using UCINET), by Stephen P. Borgatti, Martin G. Everett and Jeffrey C. Johnson (2013).
2. 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).

Courses

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

Datasets

1. KONECT - The Koblenz Network Collection.

• Stanford Large Network Dataset Collection.
• tnet Datasets.
• UCI Network Data Repository.

Journals

1. Connections.

• Journal of Social Structure.
• Network Science.
• REDES, in Spanish.
• Social Network Analysis and Mining.
• Social Networks.

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 (mailing-list).
• NetSci: Network Science Society.

Review Articles

Software

1. 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.
• 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.
• 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.
• 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.
• VOSviewer - Cross-platform software tool for constructing and visualizing bibliometric networks, written in Java.

JavaScript Libraries

• 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.

Python Libraries

Most items below are from a Google spreadsheet by Michał Bojanowski and others.

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 Packages

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

• Bergm - Tools to analyse Bayesian exponential random graph models.
• ergm - Estimation of Exponential Random Graph Models.
• GERGM - Estimation and diagnosis of the convergence of Generalized Exponential Random Graph Models (GERGM).
• igraph - A collection of network analysis tools.
• 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.
• sna - Basic network measures and visualization tools.
• spnet - Methods for dealing with spatial social networks.
• statnet - The project behind many R network analysis packages.
• 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.

Tutorials

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

• Basic and Advanced Network Visualization with Gephi and R (Sunbelt 2016).
• Exponential Random Graph Models (ERGMs) Using statnet (Sunbelt 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 (Sunbelt 2013).
• Network Analysis and Visualization with R and igraph (NetSciX 2016).
• Practical Social Network Analysis With Gephi (2014).
• Static and Dynamic Network Visualization with R (PolNet 2015).
• Working with Bipartite/Affiliation Network Data in R (2012).

Varia

• Blog posts about networks by Pierre Mercklé, in French.
• Network Fact - A Twitter account about networks, graph theory, and related topics.
• Videos from the Political Networks 2009 conference.

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.
• To cite R packages, please cite the stable CRAN version when it exists.
• To cite Python packages, please cite the PyPi version when it exists.

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

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License

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