Community detection in hypergraphs through hyperedge percolation (2504.15213v2)

Abstract: Complex networks often exhibit community structure, with communities corresponding to denser subgraphs in which nodes are closely linked. When modelling systems where interactions extend beyond node pairs to arbitrary numbers of nodes, hypergraphs become necessary, creating a need for specialised community detection methods. Here, we adapt the classical $k$-clique percolation method to hypergraphs, constructing communities from hyperedges containing at least $k$ nodes, defining hyperedge adjacency similarly to clique adjacency. Although the analogy between the proposed hyperedge percolation method and the classical clique percolation algorithm is evident, we show that communities obtained directly from the hyperedges can differ from those identified via clique percolation on the pairwise projection of the hypergraph. We also propose an alternative way for merging hyperedges into communities, where instead of imposing a lower bound on hyperedge cardinality, we restrict the maximum size of the considered hyperedges. This alternative algorithm better suits hypergraphs where larger hyperedges realise weaker linkages between the nodes. After comparing the suggested two approaches on simple synthetic hypergraphs designed to highlight their distinctions, we test them on hypergraphs generated by a newly proposed geometric process on the hyperbolic plane, as well as on some real-world examples.