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briatte / awesome-network-analysis


A curated list of awesome network analysis resources.


Awesome Network Analysis Awesome DOI

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

Inspired by Awesome Deep Learning, Awesome Math and others.

Adamic and Glance’s network of political blogs, 2004.

Network of U.S. political blogs by Adamic and Glance (2004) (preprint).





Accessible introductions aimed at non-technical audiences.

General Overviews

Graph Theory





Recurring conferences on network analysis.




Journals that are not fully open-access are marked as “gated”. Please also note that some of the publishers listed below are deeply hurting scientific publishing.

Professional Groups

Research Groups (USA)

Network-focused research centers, (reading) groups, institutes, labs – you name it – based in the USA.

Research Groups (Other)

Network-focused research centers, (reading) groups, institutes, labs – you name it – based outside of the USA.

Review Articles

Archeological and Historical Networks

See also the bibliographies by Claire Lemercier and Claire Zalc (section on ‘études structurales’), by the Historical Network Research Group, and by Tom Brughmans.

Bibliographic, Citation and Semantic Networks

Biological, Ecological and Disease Networks

Complex Networks

Ethics of Network Analysis

Network Modeling

Network Visualization

Social, Economic and Political Networks

See also the bibliographies by Eszter Hargittai, by Pierre François and by Pierre Mercklé.

Selected Papers

A voluntarily short list of applied, epistemological and methodological articles, many of which have become classic readings in network analysis courses. Intended for highly motivated social science students with little to no prior exposure to network analysis.


For a hint of why this section of the list might be useful to some, see Mark Round’s Map of Data Formats and Software Tools (2009).
Several links in this section come from the NetWiki Shared Code page, from the Cambridge Networks Network List of Resources for Complex Network Analysis, and from the Software for Social Network Analysis page by Mark Huisman and Marijtje A.J. van Duijn. For a recent academic review on the subject, see the Social Network Algorithms and Software entry of the International Encyclopedia of Social and Behavioral Sciences, 2nd edition (2015).
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 The Wikipedia English entry on Social Network Analysis Software also links to many commercial that are often very expensive, outdated, and far from being awesome by any reasonable standard.
Software-centric tutorials are listed below their program of choice: other tutorials are listed in the next section.


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.

C / C++

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


  • GraphStream - Java library for the modeling and analysis of dynamic graphs.


For more awesome JavaScript libraries, see the Awesome JavaScript list.




Many items below are from a Google spreadsheet by Michał Bojanowski and others.
See also Social Network Analysis with Python, a 3-hour tutorial by Maksim Tsvetovat and Alex Kouznetsov given at PyCon US 2012 (code).
For more awesome Python packages, see the Awesome Python and Awesome Python Books lists.

  • graph-tool - Python module for network manipulation and analysis, written mostly in C++ for speed.
  • graphviz - Python renderer for the DOT graph drawing language.
  • hiveplot - Python utility for drawing networks as hive plots on matplotlib, a more comprehensive network visualization.
  • linkpred - Assess the likelihood of potential links in a future snapshot of a network.
  • metaknowledge - Python package to turn bibliometrics data into authorship and citation networks.
  • networkx - Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.
  • Implementing an ERGM from Scratch in Python, using networkx and numpy (2014).
  • nxviz - Visualization package for NetworkX.
  • npartite - Python algorithms for community detection in n-partite networks.
  • PyGraphistry - Python library to extract, transform, and visually explore big graphs.
  • python-igraph - Python version of the igraph network analysis package.
  • python-louvain - A solid implementation of Louvain community detection algorithm.
  • - A Python interface for SNAP (a general purpose, high performance system for analysis and manipulation of large networks).
  • SnapVX - A convex optimization solver for problems defined on a graph.
  • TQ (Temporal Quantities) - Python 3 library for temporal network analysis.


For more awesome R resources, see the Awesome R and Awesome R Books lists. See also this Google spreadsheet by Ian McCulloh and others.
To convert many different network model results into tidy data frames, see the broom package. To convert many different network model results into LaTeX or HTML tables, see the texreg package.

  • amen - Additive and multiplicative effects models for relational data.
  • Bergm - Tools to analyse Bayesian exponential random graph models (BERGM).
  • bipartite - Functions to visualize bipartite networks and compute indices commonly used in ecological research.
  • blockmodeling - Implementats generalized blockmodeling for valued networks.
  • bnlearn - Tools for Bayesian network learning and inference (related Shiny app).
  • btergm - Tools to fit temporal ERGMs by bootstrapped pseudolikelihood. Also provides MCMC maximum likelihood estimation, goodness of fit for ERGMs, TERGMs, and stochastic actor-oriented models (SAOMs), and tools for the micro-level interpretation of ERGMs and TERGMs.
  • CCAS - Statistical model for communication networks.
  • concoR - Implementation 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).
  • ERGM: edgecov and dyadcov Specifications.
  • GERGM - Estimation and diagnosis of the convergence of Generalized Exponential Random Graph Models (GERGM).
  • geomnet - Single-geometry approach to network visualization with ggplot2.
  • ggnetwork - Multiple-geometries approach to plot network objects with ggplot2.
  • ggraph - Grammar of graph graphics built in the spirit of ggplot2.
  • hergm - Estimate and simulate hierarchical exponential-family random graph models (HERGM) with local dependence.
  • hierformR – Determine paths and states that social networks develop over time to form social hierarchies.
  • igraph - A collection of network analysis tools.
  • Network Analysis and Visualization with R and igraph (2016).
  • influenceR - Compute various node centrality network measures by Burt, Borgatti and others.
  • keyplayer - Implements several network centrality measures.
  • latentnet - Latent position and cluster models for network objects.
  • lpNet - Linear programming model aimed at infering biological (signalling, gene) networks.
  • networkD3 - D3 JavaScript network graphs from R.
  • ndtv - Tools to construct animated visualizations of dynamic network data in various formats.
  • netdiffuseR - Tools to analyze the network diffusion of innovations.
  • NetSim - Simulate and combine micro-models to research their impact on the macro-features of social networks.
  • network - Basic tools to manipulate relational data in R.
  • networkdiffusion - Simulate and visualize basic epidemic diffusion in networks.
  • networkDynamic - Support for dynamic, (inter)temporal networks.
  • networksis - Tools to simulate bipartite networksgraphs with the degrees of the nodes fixed and specified.
  • PAFit - Nonparametric estimation of preferential attachment and node fitness in temporal complex networks.
  • PCIT - Implements Partial Correlation with Information Theory in order to identify meaningful correlations in weighted networks, such as gene co-expression networks.
  • RCy3 - Interface between R and recent versions of Cytoscape.
  • RCyjs - Interface between R and Cytoscape.js.
  • qgraph - Tools to model and visualize psychometric networks; also aimed at weighted graphical models).
  • Network Model Selection Using qgraph 1.3 (2014).
  • qgraph Examples.
  • qgraph: Network Visualizations of Relationships in Psychometric Data (2012).
  • relevent - Tools to fit relational event models (REM).
  • rem - Estimate endogenous network effects in event sequences and fit relational event models (REM), which measure how networks form and evolve over time.
  • 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 constructors, measures and visualization tools.
  • SocialMediaLab - Tools for collecting social media data and generating networks from it (companion website, github repo).
  • spectralGOF - Computes 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, wiki).
  • Exponential Random Graph Models (ERGMs) Using statnet (2015).
  • Guides for Using the statnet Package (2010).
  • Modeling Valued Networks with statnet (2013).
  • tergm - Fit, simulate and diagnose models for temporal exponential-family random graph models (TERGM).
  • tnam - Tools to fit temporal and cross-sectional network autocorrelation models (TNAM).
  • 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).



Generic graph syntaxes intended for use by several programs.


Tutorials that are not focused on a single specific software package or program.


Resources that do not fit in other categories.

Blog Series

Series of blog posts on network topics.

Fictional Networks

Explorations of fictional character networks.

Network Science

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

Small Worlds

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

Two-Mode Networks

Also known as bipartite graphs.



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, Tam-Kien Duong, Jeremy Foote, Catherine Cramer, Andrej Mrvar, Patrick Doreian, Vladimir Batagelj, Eric C. Jones, Alden S. Klovdahl, James Fairbanks, Danielle Varda, Andrew Pitts, Roman Bartusiak, Koustuv Sinha, Mohsen Mosleh, Sandro Sousa, Jean-Baptiste Pressac, Patrick Connolly and Hristo Georgiev – have waived all copyright and related or neighboring rights to this work.

Thanks to Robert J. Ackland, Marc Flandreau, Eiko Fried, Wouter de Nooy, Katya Ognyanova, Camille Roth, Cosma Shalizi, Tom A.B. Snijders and Tim A. Wheeler, who helped locating some of the awesome resources featured in this list.