G-DynaDist

A distance between temporal graphs
Using the pip command you can easily install the codes to compute the distance between temporal graph pairs that we introduced in this paper. Follow the documentation for a simple introduction to the package.

Source code Documentation

EDRep

Efficient Distributed Repepresentations
Here you find our Python implementation related to our paper Efficient distributed representations beyond negative sampling in which we show how to efficiently and accurately estimate the softmax partition functions.

Source code Documentation

CoDeBetHe

Community Detection with the Bethe Hessian
Find here an efficient Julia implementation of our algorithms for community detection. Some Python correspondents can be found in the next section

Source code Documentation

Some codes used in our articles

2021

  • Nishimori meets Bethe: a spectral method for node classification in sparse weighted graphs Julia code

2020

  • Community detection in sparse time-evolving graphs with a dynamical Bethe-Hessian (NeurIPS 2020) Python code
  • A unified framework for spectral clustering in sparse graphs Python code

2019

  • Revisiting the Bethe-Hessian: Improved Community Detection in Sparse Heterogeneous Graphs (NeurIPS 2019) Python code