Multilayer Hypergraph Clustering Using the Aggregate Similarity Matrix

Kalle Alaluusua*, Konstantin Avrachenkov, B. R.Vinay Kumar, Lasse Leskelä

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationAlgorithms and Models for the Web Graph - 18th International Workshop, WAW 2023, Proceedings
EditorsMegan Dewar, François Théberge, Paweł Prałat, Przemysław Szufel, Małgorzata Wrzosek
PublisherSpringer
Pages83-98
Number of pages16
ISBN (Print)978-3-031-32295-2
DOIs
Publication statusPublished - 2023
MoE publication typeA4 Conference publication
EventWorkshop on Algorithms and Models for the Web Graph - Toronto, Canada
Duration: 23 May 202326 May 2023
Conference number: 18

Publication series

NameLecture Notes in Computer Science
Volume13894 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Workshop

WorkshopWorkshop on Algorithms and Models for the Web Graph
Abbreviated titleWAW
Country/TerritoryCanada
CityToronto
Period23/05/202326/05/2023

Keywords

  • clustering
  • community detection
  • hypergraph SBM
  • multilayer
  • planted partition
  • semidefinite programming

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