Samuel Thiriot

Monday 14 November 2016
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Software for networks analysis

The igraph package ( is a must-have. It includes basic network generators, efficient and reliable algorithms for statistical analysis - shortest pathes, clustering, centrality, etc. Stores graphs in a sparse way. Exists as a C library, a python one, and a R package. This last one is very efficient and convenient. See doc here:

SIENA, from Tom Snidjers, is devoted to the statistical analysis of longitudinal social network data (dynamics networks)

The wikipedia page lists (too ?) many softs, most of them being able to perform statistical analysis.

Networks generation

The nauty set of programs (, written by Brendan McKay, contains geng a marvellous tool able to generate all the possible networks of size N in a very efficient way. It is even able to generate only non-isomorphic networks (see the manual).

More software

Also see a list of software there:

All the bookmarks shared in this website.
Networks: manipulate them in Java
How to generate, analyze, manipulate networks in Java ? Here is a panorama (not exhustive) of the existing libraries. JUNG The JUNG framework (...)
Networks: public data
Network data is sometimes freely available online. Here are some examples. Real networks and data many sources in the KONECT site web map as a graph DOMES (...)
Networks: persist them
How, where to save graphs and networks ? The classical solution is to store graphs into files using formats as interoperable as possible; yet more and more (...)
YANG - plausible networks generation
YANG (Yet Another Network Generator) is a laboratory devoted to the reconstruction of plausible networks from local rules. It was applied to the (...)
Simple Interconnected Islands network generator
Overview The Simple Interconnect Islands (SII) network generator creates networks made of nb.islands islands. Each island is made of a random network of (...)
Growing Interconnected Islands network generator
Overview In this variation of Simple Interconnected Islands, each island is made of a growing network instead of a random network. Leads to a skewed (...)

(c) Samuel Thiriot 2012-2016
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