Mixnet

Welcome to the MixNet software website. MixNet stands for Erdös-Renyi Mixture for Networks, a probabilistic model for random graphs. This model is a clustering model based on the hypothesis that nodes of real networks are spread into hidden classes (or colors) which show specific connectivity patterns. We propose various algorithms to estimate the model’s parameters as well as probabilities of class membership for each node. We also propose a statistical criterion to select the number of classes. A complete description at http://stat.genopole.cnrs.fr/logiciels/mixnet


References in zbMATH (referenced in 10 articles )

Showing results 1 to 10 of 10.
Sorted by year (citations)

  1. Gaucher, Solenne; Klopp, Olga: Maximum likelihood estimation of sparse networks with missing observations (2021)
  2. Mariadassou, Mahendra; Matias, Catherine: Convergence of the groups posterior distribution in latent or stochastic block models (2015)
  3. Leger, Jean-Benoist; Vacher, Corinne; Daudin, Jean-Jacques: Detection of structurally homogeneous subsets in graphs (2014)
  4. Birmelé, Etienne: Detecting local network motifs (2012)
  5. Celisse, Alain; Daudin, Jean-Jacques; Pierre, Laurent: Consistency of maximum-likelihood and variational estimators in the stochastic block model (2012)
  6. Channarond, Antoine; Daudin, Jean-Jacques; Robin, Stéphane: Classification and estimation in the stochastic blockmodel based on the empirical degrees (2012)
  7. Allman, Elizabeth S.; Matias, Catherine; Rhodes, John A.: Parameter identifiability in a class of random graph mixture models (2011)
  8. Daudin, Jean-Jacques; Pierre, Laurent; Vacher, Corinne: Model for heterogeneous random networks using continuous latent variables and an application to a tree-fungus network (2010)
  9. Zanghi, Hugo; Picard, Franck; Miele, Vincent; Ambroise, Christophe: Strategies for online inference of model-based clustering in large and growing networks (2010)
  10. Picard, Franck; Miele, Vincent; Daudin, Jean-Jacques; Cottret, Ludovic; Robin, Stéphane: Deciphering the connectivity structure of biological networks using mixnet (2009) ioport