Posted on Thu 23 January 2020 in posts • Tagged with python, machine learning, graph partitionning, community detection, statistical physics

TP Network and Community Detection

the goal here is to implement different methods for partitionning a graph into many clusters. We will first focus on the graph Laplacian, and then on implementing Belief Propagation.

Spectral Clustering

The first part is to construct a fonction generating random graph with blocks (stochastic block model) in the simplest manner. Then we will look at some properties using the graph spectrum.

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