Multilayer hypergraph clustering using the aggregate similarity matrix
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.
Cite
@article{arxiv.2301.11657,
title = {Multilayer hypergraph clustering using the aggregate similarity matrix},
author = {Kalle Alaluusua and Konstantin Avrachenkov and B. R. Vinay Kumar and Lasse Leskelä},
journal= {arXiv preprint arXiv:2301.11657},
year = {2023}
}
Comments
16 pages, 3 tables. Reason for replacement on 3 Nov 2023: incorporating the possibility of non-uniform layers. Reason for replacement on 18 May 2023: improving clarity of the presentation and clarifying the contribution/novelty of the paper