Inference of hyperedges and overlapping communities in hypergraphs

Understand

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.