Hypernetwork Science via High-Order Hypergraph Walks

Understand

We propose high-order hypergraph walks as a framework to generalize graph-based network science techniques to hypergraphs.

• Edge incidence in hypergraphs is quantitative, yielding hypergraph walks with both length and width.
• Graph methods which then generalize to hypergraphs include connected component analyses, graph distance-based metrics such as closeness centrality, and motif-based measures such as clustering coefficients.
• We apply high-order analogs of these methods to real world hypernetworks, and show they reveal nuanced and interpretable structure that cannot be detected by graph-based methods.