A hyper-distance-based method for hypernetwork comparison

TL;DR

This paper introduces a hyper-distance-based method for comparing hypernetworks that considers high-order node distances, effectively distinguishing and classifying hypernetworks, including empirical ones with disrupted hyperedges.

Contribution

The paper presents a novel hyper-distance-based method (HD) that incorporates high-order information for hypernetwork comparison, outperforming existing baselines.

Findings

HD distinguishes hypernetworks generated with different parameters

HD successfully classifies hypernetworks

HD outperforms state-of-the-art baselines in disrupted hyperedge scenarios

Abstract

Hypernetwork is a useful way to depict multiple connections between nodes, making it an ideal tool for representing complex relationships in network science. In recent years, there has been a marked increase in studies on hypernetworks, however, the comparison of the difference between two hypernetworks has been given less attention. This paper proposes a hyper-distance-based method (HD) for comparing hypernetworks. This method takes into account high-order information, such as the high-order distance between nodes. The experiments carried out on synthetic hypernetworks have shown that HD is capable of distinguishing between hypernetworks generated with different parameters, and it is successful in the classification of hypernetworks. Furthermore, HD outperforms current state-of-the-art baselines to distinguish empirical hypernetworks when hyperedges are disrupted.