Hypergraph-Based Influence Maximization in Online Social Networks

Chuangchuang Zhang (), Wenlin Cheng, Fuliang Li and Xingwei Wang
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Chuangchuang Zhang: School of Information and Electrical Engineering, Ludong University, Yantai 264025, China
Wenlin Cheng: College of Computer Science and Engineering, Northeastern University, Shenyang 110169, China
Fuliang Li: College of Computer Science and Engineering, Northeastern University, Shenyang 110169, China
Xingwei Wang: College of Computer Science and Engineering, Northeastern University, Shenyang 110169, China

Mathematics, 2024, vol. 12, issue 17, 1-18

Abstract: Influence maximization in online social networks is used to select a set of influential seed nodes to maximize the influence spread under a given diffusion model. However, most existing proposals have huge computational costs and only consider the dyadic influence relationship between two nodes, ignoring the higher-order influence relationships among multiple nodes. It limits the applicability and accuracy of existing influence diffusion models in real complex online social networks. To this end, in this paper, we present a novel information diffusion model by introducing hypergraph theory to determine the most influential nodes by jointly considering adjacent influence and higher-order influence relationships to improve diffusion efficiency. We mathematically formulate the influence maximization problem under higher-order influence relationships in online social networks. We further propose a hypergraph sampling greedy algorithm (HSGA) to effectively select the most influential seed nodes. In the HSGA, a random walk-based influence diffusion method and a Monte Carlo-based influence approximation method are devised to achieve fast approximation and calculation of node influences. We conduct simulation experiments on six real datasets for performance evaluations. Simulation results demonstrate the effectiveness and efficiency of the HSGA, and the HSGA has a lower computational cost and higher seed selection accuracy than comparison mechanisms.

Keywords: influence maximization; hypergraph; random walk; Monte Carlo (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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