Mixture of multilayer stochastic block models for multiview clustering
This method addresses multiview clustering for data integration, but it appears incremental as it builds on existing SBM frameworks.
The authors tackled the problem of aggregating multiple clusterings from different sources by proposing a mixture of multilayer Stochastic Block Models, which groups co-membership matrices and partitions observations, with results validated on synthetic data and applied to global food trading networks.
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.