Communities detection in complex networks and other tools
Radatools is a set of freely distributed programs to analyze Complex Networks. In particular, it includes very useful programs for Communities Detection and Mesoscales Determination.
The list of programs included in the current version of Radatools is:
It is prepared to work with:
- unweighted  and weighted  networks
- undirected  and directed  networks
- positive  and signed  networks
- exhaustive search
- tabu search 
- extremal optimization 
- spectral optimization 
- fast algorithm 
- fine-tuning by reposition
- fine-tuning by bootstrapping based on tabu search 
It is possible to add a common self-loop to all nodes to analyze the communities at any resolution level .
Please be kind to cite the corresponding articles when you use these tools.
Implements the strategy in [5,9] for the determination of the community structure of complex networks at different resolution levels, thus finding the whole mesoscale, from all nodes in one community (macroscale) to every node forming its own community (microscale). It is based on the addition of a common self-loop to all nodes, and the optimization of modularity [1,2,3] using the same heuristics as in Communities_Detection.
Fine-tuning of the mesoscales found by Mesoscales_Search.
Elimination of simple and triangular hairs of a network .
Convert a partition of a sized reduced network  into a partition of the original network.
Calculate many global, nodes and edges properties of a network, e.g. degrees, strengths, clustering coefficients, assortativity, connectedness, shortest path lengths, diameter, betweenness, degree distribution, distances, etc. It works with weighted and unweighted, directed and undirected, positive and signed networks.
Calculate similarity and dissimilarity indices between two partitions, e.g. Jaccard Index, Rand Index, Normalized Mutual Information, Variation of Information, etc.
Calculate the minimum and maximum spanning tree of a graph.
Converts a file with the list of links of a graph into a network file in Pajek format.
Converts a file with a graph in matrix form into a network file in Pajek format.
Converts a file with a network in Pajek format into a graph file in matrix form.
Splits a network in Pajek format into its weak or strong connected components.
Extract subgraphs from a graph given the lists of nodes which form the subgraphs.
Converts a file with a partition in Lol format into a file with a partition in Pajek format.
Converts a file with a partition in Pajek format into a file with a partition in Lol format.
Calculates the modularity of a partition of a network.
Reformat partitions in Pajek and Lol formats changing indices of nodes by names of nodes.
- Windows 32 and 64 bits: radatools-3.2-win32.zip
- Linux 32 and 64 bits: radatools-3.2-linux32.tar.gz
Mac OS X: radatools-3.2-mac.tar.gz
- README: txt, pdf
- CHANGES: txt, pdf
- LICENSE: txt, pdf
No installation needed, just uncompress the downloaded file. See the README file for the information to run each program.
M.E.J. Newman and M. Girvan
Finding and evaluating community structure in networks
Physical Review E 69 (2004) 026113
Analysis of weighted networks
Physical Review E 70 (2004) 056131
Alex Arenas, Jordi Duch, Alberto Fernández and Sergio Gómez
Size reduction of complex networks preserving modularity
New Journal of Physics 9 (2007) 176
(pdf) (doi) (IOP open access)
Sergio Gómez, Pablo Jensen and Alex Arenas
Analysis of community structure in networks of correlated data
Physical Review E 80 (2009) 016114
(pdf) (doi) (APS)
Alex Arenas, Alberto Fernández and Sergio Gómez
Analysis of the structure of complex networks at different resolution levels
New Journal of Physics 10 (2008) 053039
(pdf) (doi) (IOP open access)
Jordi Duch and Alex Arenas
Community detection in complex networks using extremal optimization
Physical Review E 72 (2005) 027104
Modularity and community structure in networks
Proc. Nat. Acad. Sci. USA 103 (2006) 8577
Fast algorithm for detecting community structure in networks
Physical Review E 69 (2004) 066133
Clara Granell, Sergio Gómez and Alex Arenas
Mesoscopic analysis of networks: applications to exploratory analysis and data clustering
Chaos 21 (2011) 016102
(pdf) (doi) (AIP)