Multilayer hypergraph clustering using the aggregate similarity matrix

Autor:	Alaluusua, Kalle, Avrachenkov, Konstantin, Kumar, B. R. Vinay, Leskelä, Lasse
Rok vydání:	2023
Předmět:	Mathematics - Statistics Theory Computer Science - Information Theory Mathematics - Probability Statistics - Machine Learning 05C65 05C80 62H30 90B15 90C22 90C35 94A16
Zdroj:	In: Dewar, M., Pra{\l}at, P., Szufel, P., Th\'eberge, F., Wrzosek, M. (eds) Algorithms and Models for the Web Graph. WAW 2023. Lecture Notes in Computer Science, vol 13894. Springer, Cham. pp. 83-98
Druh dokumentu:	Working Paper
DOI:	10.1007/978-3-031-32296-9_6
Popis:	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. Comment: 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
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2301.11657 Zobrazit plný text záznamu View this record from Arxiv