Community detection in graphs/networks project
Stochastic block model implementation, spectral clustering with the bethe hessian and comparison with other methods (adjacency matrix, Laplacian matrix, modularity matrix)
From Wikipedia:
The stochastic block model takes the following parameters:
- The number n of vertices
- a partition of the vertex set {1, ..., n} into disjoint subsets {C1, ..., Cr} called communities
- a symmetric r x r matrix P of edge probabilities. The edge set is then sampled at random as follows: any two vertices u in Ci and v in Cj are connected by an edge with probability Pij.
- Python 2.7
- Networkx
- Numpy
- Sklearn
- Scipy