文献类型：期刊文献

英文题名：Identification of important nodes in multi-layer hypergraphs based on fuzzy gravity model and node centrality distribution characteristics

作者：Wang, Peng[1];Ling, Guang[1];Zhao, Pei[2];Pan, Wenqiu[1];Ge, Ming-Feng[3]

第一作者：Wang, Peng

通讯作者：Ling, G[1]

机构：[1]Wuhan Univ Technol, Sch Math & Stat, Wuhan 430070, Peoples R China;[2]Beijing Union Univ, Teachers Coll, Beijing 100011, Peoples R China;[3]China Univ Geosci, Sch Mech Engn & Elect Informat, Wuhan 430074, Peoples R China

第一机构：Wuhan Univ Technol, Sch Math & Stat, Wuhan 430070, Peoples R China

通讯机构：[1]corresponding author), Wuhan Univ Technol, Sch Math & Stat, Wuhan 430070, Peoples R China.

年份：2024

卷号：188

外文期刊名：CHAOS SOLITONS & FRACTALS

收录：;EI(收录号：20243717028172);Scopus(收录号：2-s2.0-85203452051);WOS:【SCI-EXPANDED(收录号:WOS:001315849700001)】；

语种：英文

外文关键词：Complex networks; Multi-layer hypergraphs; Important nodes identification; Gravity model; Node centrality distribution

摘要：Hyperedge is a common structure that represents high-order interactions between nodes in complex networks, and multi-layer networks provide more diverse node interactions than single-layer networks. Therefore, multi- layer hypergraphs can more clearly represent the relationships between nodes. However, there are few studies on identifying important nodes in this framework. This paper proposes a method called HCT to fill this gap. It consists of three parts, namely: Hypergraph Fuzzy Gravity Model (HFGM), Layer Weight Calculation Method based on Node Centrality Distribution Characteristics (CDLW) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). HCT progressively analyzes the importance of a node from three levels: local (the node itself), semi-local (the hyperedges and community to which the node belongs), and global (the layer to which the node belongs). By combining these three results, the global centrality of a node in the entire network can be calculated. Simulation experiments demonstrate that the important nodes identified by HCT exhibit stronger contagion capabilities in nine networks compared to nine combinatorial methods and removing these nodes will seriously damage the connectivity and robustness of the network. The centrality of each node calculated by HCT is also consistent with its actual importance.