Influencer Identification of Threshold Models in Hypergraphs  

作　　者：Xiaojuan SONG Xilong QU Ting WEI Jilei TAI Renquan ZHANG 

机构地区：[1]School of Mathematical Sciences,Dalian University of Technology,Liaoning 116024,P.R.China

出　　处：《Journal of Mathematical Research with Applications》2024年第5期569-582,共14页数学研究及应用（英文版）

基　　金：Supported by the National Natural Science Foundation of China(Grant No.12371516);the Natural Science Foundation of Liaoning Province(Grant No.2022-MS-152);the Fundamental Research Funds for the Central Universities(Grant No.DUT22LAB305)。

摘　　要：This paper mainly studies the influence maximization problem of threshold models in hypergraphs,which aims to identify the most influential nodes in hypergraphs.Firstly,we introduce a novel information diffusion rule in hypergraphs based on Threshold Models and conduct the stability analysis.Then we extend the CI-TM algorithm,originally designed for complex networks,to hypergraphs,denoted as the H-CI-TM algorithm.Secondly,we use an iterative approach to get the globally optimal solutions.The analysis reveals that our algorithm ultimately identifies the most influential set of nodes.Based on the numerical simulations,HCI-TM algorithm outperforms several competing algorithms in both synthetic and real-world hypergraphs.Essentially,when provided with the same number of initial seeds,our algorithm can achieve a larger activation size.Our method not only accurately assesses the influence of individual nodes but also identifies a set of nodes with greater impact.Furthermore,our results demonstrate good scalability when handling intricate relationships and large-scale hypergraphs.The outcomes of our research provide substantial support for the applications of the threshold models across diverse fields,including social network analysis and marketing strategies.

关 键 词：HYPERGRAPH threshold model influence maximization information diffusion sub-critical path 

分 类 号：O157.5[理学—数学]