noisySBM - Noisy Stochastic Block Mode: Graph Inference by Multiple Testing
Variational Expectation-Maximization algorithm to fit the noisy stochastic block model to an observed dense graph and to perform a node clustering. Moreover, a graph inference procedure to recover the underlying binary graph. This procedure comes with a control of the false discovery rate. The method is described in the article "Powerful graph inference with false discovery rate control" by T. Rebafka, E. Roquain, F. Villers (2020) <arXiv:1907.10176>.
Last updated 4 years ago
2.00 score 2 scripts 164 downloadsgraphclust - Hierarchical Graph Clustering for a Collection of Networks
Graph clustering using an agglomerative algorithm to maximize the integrated classification likelihood criterion and a mixture of stochastic block models. The method is described in the article "Model-based clustering of multiple networks with a hierarchical algorithm" by T. Rebafka (2022) <arXiv:2211.02314>.
Last updated 1 years ago
1.43 score 27 scripts 212 downloads