Much of the work done in the Complex Systems Group that involves empirical networks modeling or method development also involves computational problems that cannot be solved with existing software. We often develop novel algorithms and techniques to push the boundaries of analysis of complex systems. In this page the results of that work is shared in hope that these tools will benefit the research community and other people interested in complex networks.

See our GitHub page for software we have published.

EDENetworks: An easy to use analysis tool for genetic networks

EDENetworks is an easy to use tool for analysing genetic networks. It allows researchers to use the tools from complex networks science to analyse genetic data using a user friendly graphical interface. You can study both individual level or population level networks by giving either a genetic marker file or a distance matrix as an input. EDENetworks was developed as a part of the EDEN project.

See the EDENetworks homepage:

License: GPL2

LCElib: a C++ library for efficient network analysis.

The LCElib is a c++ library consisting of weighted network data structures and analysis methods. It also contains some efficient implementations of various network models. The library was developed in order to handle large data sets and work as an efficient backbone for developing new network models. It is in active use in our research group.

The source code can be found from here: lcelib_1.0.tar.gz

Publication regarding the data structures:
Efficient data structures for sparse network representation, J. Hyvönen, J. Saramäki, and K. Kaski, Int. J. Comp. Math., Vol. 85, Issue 8, pp. 1219-1233 (2008)

License: GPL2

Sequential clique percolation algorithm

The sequential clique percolation algorithm is method for detecting clique percolation communities. It is an alternative to CFinder: instead of finding maximal cliques in a graph and producing communities of all the possible clique sizes the SCP algorithm finds all the cliques of single size and produces the community structure for all the possible thresholds. The SCP algorithm should work well even for large sparse networks, but might have trouble for dense networks and cliques of large size.

Publication: A sequential algorithm for fast clique percolation, J.M. Kumpula, M. Kivelä, K. Kaski,and J. Saramäki, Phys. Rev. E 79, 026109 (2008), arXiv:0805.1449

C++ implementation (recommended for large or dense networks): scp_0.1.tar.gz

Python implementation: