Inference of hyperedges and overlapping communities in hypergraphs.

ABSTRACT: Hypergraphs, encoding structured interactions among any number of system units, have recently proven a successful tool to describe many real-world biological and social networks. Here we propose a framework based on statistical inference to characterize the structural organization of hypergraphs. The method allows to infer missing hyperedges of any size in a principled way, and to jointly detect overlapping communities in presence of higher-order interactions. Furthermore, our model has an efficient numerical implementation, and it runs faster than dyadic algorithms on pairwise records projected from higher-order data. We apply our method to a variety of real-world systems, showing strong performance in hyperedge prediction tasks, detecting communities well aligned with the information car

SUBMITTER: Contisciani M 

PROVIDER: S-EPMC9700742 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature