Flow stability for dynamic community detection

Dynamic community detection in temporal networks using the flow stability framework described in

Alexandre Bovet, Jean-Charles Delvenne & Renaud Lambiotte, Flow stability for dynamic community detection, Sci. Adv., 8 (19), eabj3063. DOI: 10.1126/sciadv.abj3063

Requirements:

• Python3 (>=3.6)
• pandas
• scipy
• numpy
• Cython (optional but highly recommended)
• sparse_dot_mkl (optional, allows to perform multithreaded sparse matrix multiplication)

Installation

git clone git@github.com:alexbovet/flow_stability.git

cd flow_stability

python setup.py build_ext --inplace to compile the cython functions.

Usage

The main classes are:

• ContTempNetwork in the module TemporalNetwork which is used to store and save temporal networks and to compute inter-event transition matrices.
• FlowIntegralClustering in the module FlowStability which is used to computed the flow stability (integral of covariance) and to find the best forward and backward partition using the Louvain algorithm.

Additional interesting classes and functions are:

• Clustering and SparseClustering in FlowStability can be used to directly cluster covariances or intergrals of covariances.
• static_clustering in FlowStability is an helper function to cluster static networks using Markov Stability.
• run_multi_louvain in FlowStability helper function to run the Louvain multiple times on the same covariance in order to check the robustness of the partition.
• avg_norm_var_information in FlowStability computes the average Normalized Variation of Information of list of cluster lists obtained with run_multi_louvain.
• compute_parallel_clustering in parallel_clustering, same than run_multi_louvain but in parallel.
• the parallel_expm module contains functions to compute the matrix exponential of very large matrices using different strategies.

A jupyter notebook reproducing the example from Fig. 2 of the paper is available in asymmetric_example.ipynb.