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

doi:10.1109/ACCESS.2021.3056087

IR > 计算机与通信学院

	Identifying Top-k Influential Nodes Based on Discrete Particle Swarm Optimization With Local Neighborhood Degree Centrality
	Han, Lihong 1,4; Zhou, Qingguo 1; Tang, Jianxin2 ; Yang, Xuhui 3; Huang, Hengjun 4
	2021
发表期刊	IEEE ACCESS
ISSN	2169-3536
卷号	9 页码:21345-21356
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.
关键词	Social networking (online) Particle swarm optimization Optimization Search problems Integrated circuit modeling Heuristic algorithms Social sciences Discrete particle swarm optimization local search strategy neighbourhood degree centrality top-k influential nodes social network
DOI	10.1109/ACCESS.2021.3056087
收录类别	EI ; SCOPUS ; SCIE
语种	英语
WOS研究方向	Computer Science ; Engineering ; Telecommunications
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:000616295700001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
来源库	WOS
引用统计	被引频次：7[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/147360
专题	计算机与通信学院
通讯作者	Zhou, Qingguo
作者单位	1.Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China; 2.Lanzhou Univ Technol, Sch Comp & Commun, Lanzhou 730050, Peoples R China; 3.Chinese Acad Sci, Inst Modern Phys, Lanzhou 730000, Peoples R China; 4.Lanzhou Univ Finance & Econ, Sch Stat, Lanzhou 730020, Peoples R 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.