I have developed pyGAlib, a library for the analysis of graphs and complex networks in
Python, using NumPy. The library was designed for easy use and extension, with the code
and the algorithms clearly exposed.

The results of our publication "Sizing complex networks" with Romain Brasselet gave rise to
PathLims, a package to estimate the largest and the smallest pathlength (efficiency) of
graphs and digraphs, including methods to generate extremal graphs. The package is fully
compatible with pyGAlib but it is released independently.

Together with Matthieu Gilson and Nikos E. Kouvaris, we have developed a novel approach
to study complex networks based on the propagation of noisy perturbations. The package
NetDynFlow was created to calculate the spatio-temporal flow of such perturbations and
study the network properties consequently.

Application of NetDynFlow for the study of functional brain connectivity based on fMRI
activity often requires whole-brain Effective Connectivity to be estimated before. The
package pyMOU does so, assuming the multivariate Ornstein-Uhlenbeck process as the
generative model for the fMRI activity.