Awesome Network Analysis

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

Inspired by Awesome Deep Learning, Awesome Math and others.

Table of Contents

• Books
  • General Overviews
  • Graph Theory
  • Method-specific
  • Software-specific
  • Topic-specific
• Conferences
• Courses
• Datasets
• Journals
• Professional groups
  • Research Centers
  • Research Groups
• Review Articles
• Software
  • Algorithms
  • C / C++
  • JavaScript
  • MATLAB
  • Python
  • R
  • Syntaxes
• Tutorials
• Varia
  • Blog Series
  • Fictional Networks
  • Network Science
  • Small Worlds
• Contribution Guidelines

Books

General Overviews

1. Linked: The New Science of Networks, by Albert-László Barabási - Selected chapters online (2002).

• Network Science, by Albert-László Barabási - Full book online (2016).
• Network Science, by the U.S. National Research Council - Full book online (2005).
• 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 - Full pre-publication draft (2010).
• Réseaux sociaux et structures relationnelles, by Emmanuel Lazega, in French (2014).
• Sociologie des réseaux sociaux, by Pierre Mercklé, in French (2011).
• Social and Economic Networks, by Matthew O. Jackson (2008).
• Social Network Analysis. Methods and Applications, by Stanley Wasserman and Katherine Faust (1994).
• The Structure and Dynamics of Networks, edited by Mark E.J. Newman, Albert-László Barabási and Duncan J. Watts - 600 pages of classic network analysis articles (2006).
• Studying Social Networks. A Guide to Empirical Research, by Marina Hennig et al. (2013).

Graph Theory

1. Graph Theory, by Reinhard Diestel - Full book online (2000).

Method-specific

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

• Inferential Network Analysis, by Skyler J. Cranmer, Bruce A. Desmarais and Jason Morgan (forthcoming).
• Multilevel Network Analysis for the Social Sciences, edited by Emmanuel Lazega and Tom A.B. Snijders (2016).
• Network Analysis: Methodological Foundations, edited by Ulrik Brandes and Thomas Erlebach - Covers network centrality, clustering, blockmodels, spatial networks and more (2005).
• 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. Comparing Policy Networks. Labor Politics in the U.S., Germany, and Japan, by David Knoke et al. (1996).
2. Dynamical Processes on Complex Networks, by Alain Barrat, Marc Barthélemy and Alessandro Vespignani (2008).

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

Conferences

1. European Conference on Social Networks (EUSN).

• PolNet - Annual Political Networks Workshops and Conference (APSA).
• Videos from the Political Networks 2009 Conference.
• NetSci (Network Science Society).
• Sunbelt (INSNA).

Courses

1. CS 8803-NS: Network Science, by Constantine Dovrolis - Mostly open access readings (Georgia Tech, 2015).

• Complex Networks, by Peter Sheridan Dodds (University of Vermont, 2016).
• Graph Theory and Applications, by Paul Van Dooren - Full lecture slides (Hamilton Institute, Dublin, 2009).
• Graph Theory: Penn State Math 485 Lecture Notes, by Christopher Griffin - Full lecture notes (Penn State University, 2012).
• Networks, Complexity and Its Applications (Media Arts and Sciences), by Cesar Hidalgo (MIT, 2011).
• Networks, Crowds and Markets, by David Easley, Jon Kleinberg and Eva Tardos (presentation; Cornell University via edX, 2016).
• Networks (Economics), by Daron Acemoglu and Asu Ozdaglar (MIT, 2009).
• Network Science (Physics), by Albert-László Barabási, Sean Cornelius and Roberta Sinatra (Northeastern University, 2015).
• Political Networks: Methods and Applications, by Zeev Maoz (University of California in Davis, 2012).
• Social and Economic Networks: Models and Analysis, by Matthew O. Jackson (Stanford University via Coursera, 2015).
• Social Network Analysis, by Lada Adamic (University of Michigan via Coursera, not yet run).
• The Structure of Information Networks, by Jon Kleinberg - Links to many diverse readings (Cornell University, 2008).

Datasets

See also Mangal, an online platform and collection of tools to analyze, archive and share ecological network data (preprint, Python package, R package).

1. Barabási and Albert Network Datasets.

• Bill Cosponsorship Networks in European Parliaments.
• Enron Email Dataset.
• Eric D. Kolaczyk's Network Datasets.
• Gephi Datasets.
• igraphdata (R data-centric package).
• KONECT - The Koblenz Network Collection.
• James H. Fowler's Cosponsorship Network Data Page.
• Manlio De Domenico's Multilayer Networks.
• Mark E.J. Newman's Network Data.
• Pajek Datasets.
• Siena Datasets.
• Stanford Large Network Dataset Collection.
• tnet Datasets.
• UCI Network Data Repository.

Journals

Journals that are not fully open-access are marked as "gated".

1. Applied Network Science (Springer Open).

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

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.

Research Centers

1. Center for Network Science, Central European University, Budapest - Features a PhD in Network Science program.

• Indiana University Network Science Institute (IUNI).
• Interdisciplinary Center for Network Science and Applications (iCeNSA) at the University of Notre Dame.
• LINKS Center for Social Network Analysis at the Gatton College of Business and Economics, University of Kentucky.
• NetSCI Lab at the Rutgers School of Communication and Information.
• Network Science Center at the U.S. Military Academy (USMA) in West Point (blog).
• Network Science IGERT at the University of California at Santa Barbara (UCSB) - Features an NSF-funded graduate programme.
• Northeastern University Network Science Institute - Features a PhD in Network Science program..
• Northeastern University Center for Complex Network Research - Led by Albert-László Barabási.
• Northeastern University MOBS Lab - Laboratory for the Modeling of Biological and Socio-technical Systems - Led by Alessandro Vespignani.
• Warren Center for Network & Data Sciences at the University of Pennsylvania.
• Yale Institute for Network Science (YINS).

Research Groups

1. Annenberg Networks Network - Research group studying social networks at the University of Southern California.

• Complex Human Networks Reading Group (CoHN) - Reading list from a seminar held at MIT in 2001–2.
• Cx-Nets - Virtual collaboration between four complex networks research groups.
• Interdependence in Governance and Policy Research Group - Led by Bruce A. Desmarais at Penn State University.
• Large Graphs and Networks - Research group at the Catholic University of Louvain.
• Peter J. Mucha's Research Group at the University of North Carolina at Chapel Hill.
• Social Network Analysis Group @ Stanford.
• SocioPatterns - Interdisciplinary research group that uses wireless sensors to study social network data.

Review Articles

1. "Biological Networks" (Handbook of Graph Drawing and Visualization, 2013).

• "Graph Theory and Networks in Biology" (preprint; IET Systems Biology, 2007).
• "Network Analysis and Political Science" (Annual Review of Political Science, 2011).
• "Networks and History" (Complexity, 2002).
• "Social Network Algorithms and Software" (International Encyclopedia of Social and Behavioral Sciences, 2015).
• "Social Network Analysis in the Study of Terrorism and Political Violence" (preprint; PS: Political Science and Politics, 2011).
• "Statistical Models for Social Networks" (Annual Review of Sociology, 2011).

Software

Several links in this section come from the NetWiki Shared Code page.
See also the Social Network Analysis Project Survey (blog post), an earlier attempt to chart social network analysis tools that links to many commercial platforms not included in this list, such as Detective.io.
Also note that the Wikipedia English entry on Social Network Analysis Software links to many commercial that are often very expensive, outdated, and far from being awesome by any reasonable standard.

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.
• GLEAMviz Simulator - Cross-platform tool intended for the prediction of human epidemics.
• Graphviz - Cross-platform software to draw graphs in the DOT graph drawing language.
• MuxViz - Cross-platform, free and open source tool to study multilayer networks, based on R and GNU Octave.
• Neo4j - Open source, scalable graph database, used by companies like Linkurious.
• NodeXL - Free, open-source template to explore network graphs with Microsoft Excel.
• 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.
• Radatools - Set of tools intended for the analysis of complex networks, built on top of Radalib, a library written in Ada.
• Siena - Simulation Investigation for Empirical Network Analysis. Formerly a Windows program, now developed as the RSiena R package.
• SoNIA (Social Network Image Animator) - Tool to visualize dynamic or longitudinal network data. Formerly a Java program, now developed as the ndtv R package.
• 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 Java tool for constructing and visualizing bibliometric networks.

Algorithms

Network placement and community detection algorithms that do not fit in any of the next subsections.
See also the Awesome Algorithms and Awesome Algorithm Visualization lists for more algorithmic awesomess.

1. CONGA and CONGO - Algorithms to detect overlapping communities in networks, written in Java.

• ForceAtlas2 - Force-directed layout included in Gephi (paper).
• vbmod: Variational Bayesian Inference for Network Modularity - MATLAB and Python implementations of a Bayesian community detection algorithm.
• weighted-modularity-LPAwbPLUS - Julia, MATLAB and R implementations of two algorithms to find weighted modularity in bipartite networks.

C / C++

For more awesome C / C++ content, see the Awesome C and Awesome C / C++ lists.

1. Benchmark Graphs to Test Community Detection Algorithms - C++ code to generate weighted and unweighted graphs.

• Boost Graph Library (BGL) - C++ library that provides a generic interface to access graph structures.
• igraph - C library of network analysis tools; also exists as packages for Python and R.
• Louvain Method - C++ code for the Louvain multi-level community detection algorithm.
• networks.tb - C program designed for analyzing socio-semantic networks. Runs on Linux and Mac OS X.
• OpenOrd: Large-scale Graph Layout (formerly DrL) - Written in C++. Also available as a Gephi plugin.
• Stanford Network Analysis Project - C++ general purpose network analysis and graph mining library. Available as a Python library and in Microsoft Excel via NodeXL.
• Walktrap - C++ program that implements the WalkTrap community detection algorithm.

JavaScript

For more awesome JavaScript libraries, 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.
• VivaGraphJS - Graph drawing library (ForceAtlas2 plugin).
• viz.js - Use Graphviz in Web pages.

MATLAB

1. CONTEST - Random network toolbox that implements nine network models.

• Generalized Louvain - A variant of the Louvain community detection algorithm.
• MatlabBGL - A graph library for Matlab, based on the Boost graph library.
• MATLAB RBN Toolbox - Simulation und visualization of Random Boolean Networks.

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. graph-tool - Python module for network manipulation and analysis, written mostly in C++ for speed.

• 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 resources, see the Awesome R and Awesome R Books lists.

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).
• ContentStructure - implements an extension to the Topic-Partitioned Multinetwork Embeddings (TPME) model.
• DiagrammeR - Connects R, RStudio and JavaScript libraries to draw graph diagrams (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 software like Gephi or Sigma.
• Rgraphviz - Support for using the Graphviz library and its DOT graph drawing language from within R.
• RSiena - Simulation Investigation for Empirical Network Analysis; fits models to longitudinal network data.
• sna - Basic network measures and visualization tools.
• spectralGOF - Compute the "spectral goodness of fit" (SGOF), a measure of how well a network model explains the structure of an observed network.
• spnet - Methods for dealing with spatial social networks.
• statnet - The project behind many R network analysis packages (mailing-list).
• 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).

Syntaxes

Generic graph syntaxes intended for use by several programs.

1. DOT - Graph drawing syntax used by the Graphviz software.

• GEXF - File format used by the Gephi software.
• JUNG — Java Universal Network/Graph Framework - Extensible library to represent network objects.
• GraphML - Comprehensive and easy-to-use file format for graphs (handbook chapter).
• PGF/TikZ - Tandem of vector graphics languages that can be used to draw graphs in the LaTeX typesetting environment.
• Awesome LaTeX: TiKZ.
• How to Draw Graphs in LaTeX?
• TikZ Graph Examples.
• TikZ & PGF Manual.
• TKZ - Packages based on TikZ.

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

This section is for blog posts, videos, wikis and everything else that does not fit in other categories.

1. A Sociology Citation Network and A Co-citation Network for Philosophy - Examples of scientific co-citation networks.

• Basic Notions for the Analysis of Large Two-mode Networks - Introduction to bipartite network analysis (related code).
• David Knoke on Network Analysis - A 20-minute interview that discusses the uses and benefits of network analysis, drawing upon Knoke's research on terrorist networks.
• NetWiki - Wiki of various network analysis resources.
• Network Fact - Twitter account about networks, graph theory, and related topics.
• Tarot cards for Principles of Complex Systems and Complex Networks - Part of a course by Peter Sheridan Dodds.
• The Performativity of Networks - A paper that connects network theory to the sociology of science.

Blog Series

Series of blog posts on network topics.
For more blog posts on manipulating networks with R, try searching for ‘networks’ or ‘social network analysis’ on the R-Bloggers R blogs aggregator.

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

• Blog Posts About Networks by François Briatte, in French.
• Blog Posts About Networks by Katya Ognyanova.
• Blog Posts About Networks by Pierre Mercklé, in French.
• Blog Posts About Networks on the Bad Hessian Blog, by various contributors.
• Cosma Shalizi's notebooks on network-related topics, definitely worth listing in (selective) detail:
  • Analysis of Network Data.
  • Assortative Social Networks and Neutral Cultural Evolution.
  • Biochemical Network Evolution.
  • Citations and Citation Networks.
  • Community Discovery Methods for Complex Networks.
  • Complex Networks.
  • Experiments on Social Networks.
  • Exponential Random Graph Models (ERGMs).
  • Graph Sampling Algorithms.
  • Graph Theory.
  • Homophily and Influence in Social Networks.
  • Inferring Networks from Non-Network Data.
  • Joint Modeling of Texts and Networks.
  • Network Comparison.
  • Networks of Political Actors.
  • Relational Learning.
  • Social Contagion, Information Cascades, Diffusion of Innovations, Etc.
  • Social Networks.
  • Stochastic Block Models.
• Networks Demystified, a series of blog posts by Scott B. Weingart.
• R / Notes: Networks - Blog posts focused on manipulating networks in R, by François Briatte.

Fictional Networks

Explorations of fictional character networks.

1. Lessons on Exponential Random Graph Modeling from Grey’s Anatomy hook-ups.

• Star Wars Social Networks: The Force Awakens - Example of a social network analysis written in F#.

Network Science

Discussions of what "netsci" is about and means for other scientific disciplines.

1. The Emergence of Network Science - Video documentary, featuring Steven Strogatz and many others.

• Three Hard Questions about Network Science.
• What is Network Science? - First editorial of the recent Network Science journal.

Small Worlds

Links focused on (analogues to) Stanley Milgram's small-world experiment.

1. The Erdös Number Project - Research project on the collaborative ties and network distance between mathematicians.

• How Small is the World, Really? - Discussion of "x degrees of separation" small-world experiments.
• The Oracle of Bacon - Based on an online game that resulted in a charity.
• Panel: Six Degrees of Separation - Video of a conference at Cornell University, featuring Duncan Watts, Steven Strogatz, Jon Kleinberg and other speakers.
• Patterns in the Ivy: The Small World of Metal - Example of a two-mode network analysis based on metal artists and bands.
• Six Degrees of Separation - Wikipedia English entry.

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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 resources are listed alphabetically, with date and language mentions when relevant.
• Authors are required for books and courses only, but are also mentioned for blog series.
• Journals require the name of the publisher.
• Review Articles require a journal and a date.
• R packages: please consider citing the stable CRAN version when it exists.
• Python packages: please consider citing the PyPi version when it exists.
• Software should be at least free for noncommercial use (only few exceptions will be granted).

Please use U.S. English and 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 – by chronological order: François Briatte, Ian McCulloh, Aditya Khanna, Manlio De Domenico, Patrick Kaminski, Ericka Menchen-Trevino – have waived all copyright and related or neighboring rights to this work.