Mixture of multilayer stochastic block models for multiview clustering
Kylliann De Santiago Marie Szafranski Christophe Ambroise
In this work, we propose an original method for aggregating multiple
clustering coming from different sources of information. Each partition is
encoded by a co-membership matrix between observations. Our approach uses a
mixture of multilayer Stochastic Block Models (SBM) to group co-membership
matrices with similar information into components and to partition observations
into different clusters, taking into account their specificities within the
components. The identifiability of the model parameters is established and a
variational Bayesian EM algorithm is proposed for the estimation of these
parameters. The Bayesian framework allows for selecting an optimal number of
clusters and components. The proposed approach is compared using synthetic data
with consensus clustering and tensor-based algorithms for community detection
in large-scale complex networks. Finally, the method is utilized to analyze
global food trading networks, leading to structures of interest.