Feng Ruonan, Xu Tao, Xie Xiaowen, Zhang Zi-Ke, Liu Chuang, Zhan Xiu-Xiu
Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou 311121, People's Republic of China.
College of Media and International Culture, Zhejiang University, Hangzhou 310058, People's Republic of China.
Chaos. 2024 Aug 1;34(8). doi: 10.1063/5.0221267.
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 received less attention. This paper proposes a hyper-distance (HD)-based method for comparing hypernetworks. The method is based on higher-order information, i.e, the higher-order distance between nodes and Jensen-Shannon divergence. 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 randomly perturbed.
超网络是描绘节点之间多重连接的一种有用方式，使其成为网络科学中表示复杂关系的理想工具。近年来，关于超网络的研究显著增加；然而，两个超网络之间差异的比较受到的关注较少。本文提出了一种基于超距离（HD）的超网络比较方法。该方法基于高阶信息，即节点之间的高阶距离和詹森 - 香农散度。在合成超网络上进行的实验表明，HD能够区分由不同参数生成的超网络，并且在超网络分类方面取得了成功。此外，当超边被随机扰动时，HD在区分经验超网络方面优于当前最先进的基线方法。
