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arXiv · 2301.11657

Multilayer hypergraph clustering using the aggregate similarity matrix

Kalle Alaluusua·Konstantin Avrachenkov·B. R. Vinay Kumar·Lasse Leskelä
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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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Kalle Alaluusua, Konstantin Avrachenkov, B. R. Vinay Kumar, Lasse Leskelä. 2023-01-27. Multilayer hypergraph clustering using the aggregate similarity matrix. https://doi.org/10.1007/978-3-031-32296-9_6

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