Influential-nodes Identification in Hypergraphs Based on Degree-heterogeneous Hierarchical Spherical Algorithm

Identifying influential nodes in networks is an important research topic in the field of network science. Existing work focuses mainly on traditional simple networks based on pairwise interactions, which cannot effectively utilize the higher-order information of the network, resulting in an inaccurate assessment of node influence. To this end, this paper proposes a new Degree-heterogeneous Hierarchical Spherical Algorithm (DHSA), which combines the higher-order distance information in the hypergraph with Shannon entropy and can effectively quantify the mutual influence of the center node and the set of equidistant nodes. Experiments conducted on six real hypergraphs show that DHSA can effectively evaluate the influence of nodes compared with the baseline methods, and achieves 89.23% and 123.44% performance advantage over the second-ranked baseline algorithm in terms of Kendall's coefficient and the proportion of top high-influence nodes, respectively.