Identifying Top-k Influential Nodes Based on Discrete Particle Swarm Optimization with Local Neighborhood Degree Centrality

doi:10.1109/ACCESS.2021.3056087

	Identifying Top-k Influential Nodes Based on Discrete Particle Swarm Optimization with Local Neighborhood Degree Centrality
	Han, Lihong 1,4; Zhou, Qingguo 4; Tang, Jianxin 2; Yang, Xuhui 3; Huang, Hengjun 1
刊名	IEEE Access
	2021
卷号	9 页码:21345-21356
关键词	Ant colony optimization Local search (optimization) Swarm intelligence Discrete particle swarm optimization Influential individuals Local search strategy Particle swarm algorithm Pre-mature convergences State-of-the-art algorithms Suboptimal solution Swarm intelligence algorithms
DOI	10.1109/ACCESS.2021.3056087
英文摘要	The top-k influential individuals in a social network under a specific topic play an important role in reality. Identifying top-k influential nodes of a social network is still an open and deeply-felt problem. In recent years, some researchers adopt the swarm intelligence algorithm to solve such problems and obtain competitive results. There are two main algorithm models for swarm intelligence, namely Ant Colony System (ACS) and Particle Swarm Optimization (PSO). The discretized basic Particle Swarm Algorithm (DPSO) shows comparable performance in identifying top-k influential nodes of a social network. However, the performance of the DPSO algorithm is directly related to the choice of its local search strategy. The local search strategy based on the greedy mechanism of the initial DPSO can easily lead to the global suboptimal solution due to the premature convergence of the algorithm. In this paper, we adopt the degree centrality based on different neighbourhoods to enhance its local search ability. Through experiments, we find that local search strategies based on different neighbourhoods have significant differences in the improvement of the algorithm's global exploration capabilities, and the enhancement of the DPSO algorithm based on the degree centrality of different neighbourhoods has a saturation effect. Finally, based on the degree centrality of the best neighbourhood with improved local search ability, we propose the DPSO_NDC algorithm. Experimental results in six real-world social networks show that the proposed algorithm outperforms the initial DPSO algorithm and other state-of-the-art algorithms in identifying the top-k influence nodes. © 2013 IEEE.
语种	英语
出版者	Institute of Electrical and Electronics Engineers Inc.
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/147158]
专题	计算机与通信学院
作者单位	1.School of Statistics, Lanzhou University of Finance and Economics, Lanzhou; 730020, China 2.School of Computer and Communication, Lanzhou University of Technology, Lanzhou; 730050, China; 3.Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou; 730000, China; 4.School of Information Science and Engineering, Lanzhou University, Lanzhou; 730000, China;
推荐引用方式 GB/T 7714	Han, Lihong,Zhou, Qingguo,Tang, Jianxin,et al. Identifying Top-k Influential Nodes Based on Discrete Particle Swarm Optimization with Local Neighborhood Degree Centrality[J]. IEEE Access,2021,9:21345-21356.
APA	Han, Lihong,Zhou, Qingguo,Tang, Jianxin,Yang, Xuhui,&Huang, Hengjun.(2021).Identifying Top-k Influential Nodes Based on Discrete Particle Swarm Optimization with Local Neighborhood Degree Centrality.IEEE Access,9,21345-21356.
MLA	Han, Lihong,et al."Identifying Top-k Influential Nodes Based on Discrete Particle Swarm Optimization with Local Neighborhood Degree Centrality".IEEE Access 9(2021):21345-21356.