Published May 23, 2023
| Version v1
Conference paper
Multilayer Hypergraph Clustering Using the Aggregate Similarity Matrix
- Others:
- Department of Mathematics [Aalto] ; Aalto University
- Network Engineering and Operations (NEO ) ; Inria Sophia Antipolis - Méditerranée (CRISAM) ; Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
- Supported by the Vilho, Yrjö and Kalle Väisälä Foundation of the Finnish Academy of Science and Letters, and the French government through the RISE Academy of UCA Investments in the Future project managed by the National Research Agency (ANR) with the reference number ANR-15-IDEX-0001.
Description
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.
Abstract
International audience
Additional details
- URL
- https://inria.hal.science/hal-04372200
- URN
- urn:oai:HAL:hal-04372200v1
- Origin repository
- UNICA