-_[Networks, Crowds, and Markets: Reasoning About a Highly Connected World](http://www.cs.cornell.edu/home/kleinber/networks-book/)_, by David Easley and Jon Kleinberg; complete pre-publication draft online (2010).
- _[Réseaux sociaux et structures relationnelles](https://www.puf.com/content/R%C3%A9seaux_sociaux_et_structures_relationnelles)_, by Emmanuel Lazega, in French (2014).
-_[Social Network Analysis. Methods and Applications](http://www.cambridge.org/ar/academic/subjects/sociology/sociology-general-interest/social-network-analysis-methods-and-applications)_, by Stanley Wasserman and Katherine Faust (1994).
1._[Multilevel Network Analysis for the Social Sciences](https://www.springer.com/fr/book/9783319245188)_, by Emmanuel Lazega and Tom A.B. Snijders (2016).
-_[Political Networks. The Structural Perspective](http://www.cambridge.org/ar/academic/subjects/sociology/political-sociology/political-networks-structural-perspective)_, by David Knoke (1994).
1._[Analyzing Social Networks](https://sites.google.com/site/analyzingsocialnetworks/)_ (using UCINET), by Stephen P. Borgatti, Martin G. Everett and Jeffrey C. Johnson (2013).
1._[Neighbor Networks. Competitive Advantage Local and Personal](https://global.oup.com/academic/product/neighbor-networks-9780199570690)_, by Ronald S. Burt (2010).
-_[Comparing Policy Networks. Labor Politics in the U.S., Germany, and Japan](http://www.cambridge.org/ar/academic/subjects/politics-international-relations/comparative-politics/comparing-policy-networks-labor-politics-us-germany-and-japan)_, by David Knoke _et al._ (1996).
1. [AFS RT 26 “Réseaux sociaux”](http://www.cmh.pro.ens.fr/reseaux-sociaux/) - Thematic Network of the French Sociological Association, in French.
- [ECPR Political Networks SG](https://politicalnetsecpr.wordpress.com/) - Standing Group of the European Consortium for Political Research ([Twitter account](https://twitter.com/politicalnets)).
- [GDR Analyse de réseaux en sciences humaines et sociales](http://arshs.hypotheses.org/), in French.
- [Groupe FMR - Flux, Matrices, Réseaux](http://groupefmr.hypotheses.org/), in French.
- [INSNA: International Network for Social Network Analysis](http://www.insna.org/) ([mailing-list](http://www.insna.org/pubs/socnet.html)).
1. "[Network Analysis and Political Science](http://www.annualreviews.org/doi/abs/10.1146/annurev.polisci.12.040907.115949)" (_Annual Review of Political Science_, 2011).
- "[Statistical Models for Social Networks](http://www.annualreviews.org/doi/abs/10.1146/annurev.soc.012809.102709)" (_Annual Review of Sociology_, 2011).
1. [Cytoscape](http://www.cytoscape.org/) - Cross-platform Java program to build, analyze and visualize networks.
- [Discourse Network Analyzer (DNA)](http://www.philipleifeld.com/discourse-network-analyzer/discourse-network-analyzer-dna.html) - Qualitative content analysis tool with network export facilities, written in Java with R integration.
- [Pajek](http://vlado.fmf.uni-lj.si/pub/networks/pajek/) - Windows program for large network analysis, free for noncommercial use.
- [PNet](http://www.swinburne.edu.au/fbl/research/transformative-innovation/our-research/MelNet-social-network-group/PNet-software/index.html) - Simulation and estimation of exponential random graph models (ERGMs), written in Java for Windows.
- [Siena](http://www.stats.ox.ac.uk/~snijders/siena/) - Simulation Investigation for Empirical Network Analysis, formerly a Windows program, now an R package.
- [Stanford Network Analysis Project](http://snap.stanford.edu/) - C++ general purpose network analysis and graph mining library; available as a Python library and through NodeXL.
- [d3.js](https://d3js.org/) - JavaScript visualization library that can plot [force-directed graphs](http://bl.ocks.org/mbostock/4062045).
- [jLouvain](https://github.com/upphiminn/jLouvain) - Louvain community detection for Javascript ([example](http://bl.ocks.org/emeeks/125db75c9b55ddcbdeb5)).
- [Sigma](http://sigmajs.org/) - JavaScript library dedicated to graph drawing.
> Most items below are from [a Google spreadsheet](https://docs.google.com/spreadsheets/d/1vJILk2EW1JnR3YAwTSSqAV5mPkeXaezy45wOoafBpfU/edit#gid=0) by Michał Bojanowski and others.
> For more awesome Python packages, see the [Awesome Python](https://github.com/vinta/awesome-python) list.
- [networkx](http://networkx.github.io/) - Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.
> See also [this Google spreadsheet](https://docs.google.com/spreadsheets/d/1CoFGtrW85D9FsVcAE5-bcXVl6QOTncwXjFBYp4u2WgE/edit?usp=sharing) by Ian McCulloh and others.
> For more awesome R packages, see the [Awesome R](https://github.com/qinwf/awesome-R) list.
- [Bergm](https://cran.r-project.org/web/packages/Bergm/) - Tools to analyse Bayesian exponential random graph models.
- [concoR](https://github.com/aslez/concoR) - A translation of the CONCOR network blockmodeling algorithm ([blog post](http://badhessian.org/2015/05/concor-in-r/)).
- [ergm](https://cran.r-project.org/web/packages/ergm/) - Estimation of Exponential Random Graph Models.
- [GERGM](https://cran.r-project.org/web/packages/GERGM/) - Estimation and diagnosis of the convergence of Generalized Exponential Random Graph Models (GERGM).
- [hergm](https://cran.r-project.org/web/packages/hergm/) - Estimate and simulate hierarchical exponential-family random graph models with local dependence.
- [igraph](http://igraph.org/r/) - A collection of network analysis tools.
- [latentnet](https://cran.r-project.org/web/packages/latentnet/) - Latent position and cluster models for network objects.
- [networkD3](http://christophergandrud.github.io/networkD3/) - D3 JavaScript network graphs from R.
- [ndtv](https://cran.r-project.org/web/packages/ndtv/) - Tools to construct animated visualizations of dynamic network data in various formats.
- [network](https://cran.r-project.org/web/packages/network/) - Basic tools to manipulate relational data in R.
- [networkDynamic](https://cran.r-project.org/web/packages/networkDynamic/) - Support for dynamic, (inter)temporal networks.
- [rgexf](https://bitbucket.org/gvegayon/rgexf/wiki/Home) - Export network objects from R to [GEXF](http://gexf.net/format/), for manipulation with network software like Gephi or Sigma.
1. [Analyse des réseaux : une introduction à Pajek](http://quanti.hypotheses.org/512/), in French (2011).
- [Basic and Advanced Network Visualization with Gephi and R](http://kateto.net/sunbelt2016) (Sunbelt 2016).
- [Exponential Random Graph Models (ERGMs) Using statnet](https://statnet.org/trac/raw-attachment/wiki/Sunbelt2015/ergm_tutorial.html) (Sunbelt 2015).
- [Guides for Using the statnet Package](http://www.melissaclarkson.com/resources/R_guides/) (2010).
- [Implementing an ERGM From Scratch in Python](http://davidmasad.com/blog/ergms-from-scratch/) (2014).
- [L'analyse des graphes bipartis](https://halshs.archives-ouvertes.fr/FMR/halshs-00794976), in French (2013).
- [La détection de communautés avec Pajek 3.6](http://groupefmr.hypotheses.org/544), in French (2012).
- [Modeling Valued Networks with statnet](https://statnet.org/trac/raw-attachment/wiki/Sunbelt2013/Valued.pdf) (Sunbelt 2013).
- [Network Analysis and Visualization with R and igraph](http://kateto.net/networks-r-igraph) (NetSciX 2016).
- [Practical Social Network Analysis With Gephi](http://derekgreene.com/gephitutorial/) (2014).
- [Static and Dynamic Network Visualization with R](http://kateto.net/network-visualization) (PolNet 2015).
- [Working with Bipartite/Affiliation Network Data in R](https://solomonmessing.wordpress.com/2012/09/30/working-with-bipartiteaffiliation-network-data-in-r/) (2012).
Please contribute to this list by sending a [pull request](https://github.com/briatte/awesome-network-analysis/pulls) after reading the [Contribution Guidelines](https://github.com/sindresorhus/awesome/blob/master/contributing.md) for stylistic indications.
Remember that an awesome list has to be, well, awesome. The "[Awesome Manifesto](https://github.com/sindresorhus/awesome/blob/master/awesome.md)" 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.
To the extent possible under law, the authors of this list ([François Briatte](http://f.briatte.org/)) have waived all copyright and related or neighboring rights to this work.
