Abstract：In order to solve the problem of low resolution and lack of concrete and comprehensive recognition results of important nodes in hypernetworks, in this paper, combined with the degree of node, degree of transcendence, degree of adjacency and degree of adjacency, the compound information entropy is proposed to identify the important nodes of the hypernetwork. In this method, the influence coefficient is set, and the composite structure entropy of each node is obtained by analyzing the influence degree of degree of node, degree of adjacency, degree of node transcendence and degree of adjacency. Its advantage is that the influence of nodes and adjacent nodes is considered, and only the local attributes of nodes are used, resulting in lower complexity. The simulation experiments are carried out in the research cooperation hypernetwork and Kunming common bus line hypernetwork. Experimental results show that the proposed method can effectively identify the important nodes in the hypernetwork.
涂贵宇, 潘文林, 张天军. 基于信息熵的超网络重要节点识别方法[J]. 复杂系统与复杂性科学, 2025, 22(1): 18-25.
TU Guiyu, PAN Wenlin, ZHANG Tianjun. Identification Methods of Important Nodes Based on Information Entropy in Hypernetworks[J]. Complex Systems and Complexity Science, 2025, 22(1): 18-25.
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