Network science

A recurrent topic in my various projects is the presence of weighted and graphs (e.g. neuronal networks) which possess specific structures and enable the propagation of specific activity patterns.

You can find the subject page of my current lab here

I developed several theoretical and computational tools to analyse these networks.

NNGT, unified graph analysis in Python

NNGT icon NNGT is my main contribution to this field. The library enables users to read, generate, and analyse complex networks, either through custom algorithms I wrote, or using code from other libraries in the background, which I normalized to make the results consistent among libraries.

NNGT also provides domain-specific tools for neuroscience (interacting with the NEST and DeNSE simulators) and geospatial analyses.

You can find more information in the documentation or look at the code on SourceHut or GitHub.

Clustering in weighted directed networks

I am currently working on a new definition of clustering for weighted directed network, especially in the context of inferred networks or graph that possess broad weight distributions.

This new definition aims at tackling some of the issues described in [Saramäki2007] and some more. The preprint is available on arXiv and you can check my presentation at Complex Networks 2020 (video) and the one at COSYNE 2021 (video).

References

Saramäki2007

Saramäki, J., Kivelä, M., Onnela, J.-P., Kaski, K. and Kertész, J.: Generalizations of the Clustering Coefficient to Weighted Complex Networks. Phys. Rev. E 75, 027105 (2007).