Core-Periphery Detection in Hypergraphs

Core-periphery detection is a key task in exploratory network analysis where one aims to find core, set of nodes well connected internally and with the periphery, only (or mostly) core. In this work we propose model core-periphery for higher-order networks modeled as hypergraphs, method computing core-score vector that quantifies how close each node particular, show solves corresponding nonconvex optimization problem globally an arbitrary precision. This turns out coincide computation Perron eigenvector nonlinear hypergraph operator, suitably defined terms incidence matrix hypergraph, generalizing recently proposed centrality models hypergraphs. We perform several experiments on synthetic real-world hypergraphs showing outperforms alternative algorithms, particular those obtained by transferring established graph methods setting via clique expansion.