Multilayer hypergraph clustering using the aggregate similarity matrix
- 18th Workshop on Algorithms and Models for the Web Graph (WAW), 2023
- Lecture Notes in Computer Science 13894, pp. 83–98, Springer 2023
- Published online: 16 May 2023
- doi:10.1007/978-3-031-32296-9_6
- arXiv:2301.11657
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 aggregated number of hyperedge incident to each pair of vertices, represented using a similarity matrix, 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.