Multilayer hypergraph clustering using the aggregate similarity matrix

• URL: http://arxiv.org/abs/2301.11657v3
• Date: Fri, 3 Nov 2023 17:22:20 GMT
• Title: Multilayer hypergraph clustering using the aggregate similarity matrix
• Authors: Kalle Alaluusua, Konstantin Avrachenkov, B. R. Vinay Kumar, Lasse Leskel\"a
• Abstract summary: We consider the community recovery problem on a multilayer variant of the hypergraph block model (HSBM) In this work, we investigate a semidefinite programming (SDP) approach and obtain information-theoretic conditions on the model parameters that guarantee exact recovery.
• Score: 0.7373617024876725
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We consider the community recovery problem on a multilayer variant of the hypergraph stochastic block model (HSBM). Each layer is associated with an independent realization of a d-uniform HSBM on N vertices. Given the similarity matrix containing the aggregated number of hyperedges incident to each pair of vertices, the goal is to obtain a partition of the N vertices into disjoint communities. In this work, we investigate a semidefinite programming (SDP) approach and obtain information-theoretic conditions on the model parameters that guarantee exact recovery both in the assortative and the disassortative cases.

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