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Communication Dans Un Congrès Année : 2023

Multilayer Hypergraph Clustering Using the Aggregate Similarity Matrix

Résumé

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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hal-04372200 , version 1 (04-01-2024)

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Kalle Alaluusua, Konstantin Avrachenkov, B R Vinay Kumar, Lasse Leskelä. Multilayer Hypergraph Clustering Using the Aggregate Similarity Matrix. WAW 2023 - Algorithms and Models for the Web Graph, May 2023, Toronto, Canada. pp.83-98, ⟨10.1007/978-3-031-32296-9_6⟩. ⟨hal-04372200⟩
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