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Mixture of multilayer stochastic block models for multiview clustering

arXiv 2401.04682 ↗First posted 9 Jan 2024cs.LG math.ST stat.MLcited by 0

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Abstract (arXiv)

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.

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References · 15

  1. 2017Multimodal Machine Learning: A Survey and Taxonomycited by 1,004
  2. 2022Multimodal learning with graphscited by 54
  3. 2014Layer aggregation and reducibility of multilayer interconnected networkscited by 34
  4. 2020Community Detection on Mixture Multi-layer Networks via Regularized Tensor Decompositioncited by 30
  5. 2017Spectral and matrix factorization methods for consistent community detection in multi-layer networkscited by 27
  6. 2020Exact Clustering in Tensor Block Model: Statistical Optimality and Computational Limitcited by 23
  7. 2019Multiway clustering via tensor block modelscited by 17
  8. 2015Community detection in multi-relational data with restricted multi-layer stochastic blockmodelcited by 14
  9. 2015Clustering Network Layers With the Strata Multilayer Stochastic Block Modelcited by 10
  10. 2021ALMA: Alternating Minimization Algorithm for Clustering Mixture Multilayer Networkcited by 8
  11. 2021Clustering of Diverse Multiplex Networkscited by 8
  12. 2017Multilayer Spectral Graph Clustering via Convex Layer Aggregation: Theory and Algorithmscited by 7
3 more
  1. 2018The Power Mean Laplacian for Multilayer Graph Clusteringcited by 5
  2. 2020Spectral clustering via adaptive layer aggregation for multi-layer networkscited by 4
  3. 2022Sparse Subspace Clustering in Diverse Multiplex Network Modelcited by 4

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