Core-periphery detection in hypergraphs

Francesco Tudisco, Desmond J Higham

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Core-periphery detection is a key task in exploratory network analysis where one aims to find a core, a set of nodes well-connected internally and with the periphery, and a periphery, a set of nodes connected only (or mostly) with the core. In this work we propose a model of core-periphery for higher-order networks modeled as hypergraphs and we propose a method for computing a core-score vector that quantifies how close each node is to the core. In particular, we show that this method solves the corresponding non-convex core-periphery optimization problem globally to an arbitrary precision. This method turns out to coincide with the computation of the Perron eigenvector of a nonlinear hypergraph operator, suitably defined in term of the incidence matrix of the hypergraph, generalizing recently proposed centrality models for hypergraphs. We perform several experiments on synthetic and real-world hypergraphs showing that the proposed method outperforms alternative core-periphery detection algorithms, in particular those obtained by transferring established graph methods to the hypergraph setting via clique expansion.
Original languageEnglish
Pages (from-to)1-21
JournalSIAM Journal on the Mathematics of Data Science (SIMODS)
Issue number1
Early online date25 Jan 2023
Publication statusPublished - 31 Mar 2023


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