A clique-based discrete bat algorithm for influence maximization in identifying top-k influential nodes of social networks

doi:10.1007/s00500-021-05749-7

	A clique-based discrete bat algorithm for influence maximization in identifying top-k influential nodes of social networks
	Han, Lihong 2,5; Li, Kuan-Ching 3; Castiglione, Arcangelo 4; Tang, Jianxin 1; Huang, Hengjun 5; Zhou, Qingguo 2
刊名	SOFT COMPUTING
	2021
卷号	25 期号:13 页码:8223-8240
关键词	Large-scale networks Top-k influential nodes Discrete bat algorithm Clique partition Diffusion of influence
ISSN号	1432-7643
DOI	10.1007/s00500-021-05749-7
英文摘要	The problem of identifying the top-k influential node is still an open and deeply felt issue. The development of a stable and efficient algorithm to deal with such identification is still a challenging research hot spot. Although conventional centrality-based and greedy-based methods show high performance, they are not very efficient when dealing with large-scale social networks. Recently, algorithms based on swarm intelligence are applied to solve the problems mentioned above, and the existing researches show that such algorithms can obtain the optimal global solution. In particular, the discrete bat algorithm (DBA) has been proved to have excellent performance, but the evolution mechanism based on a random selection strategy leads to the optimal solution's instability. To solve this problem, in this paper, we propose a clique-DBA algorithm. The proposed algorithm is based on the clique partition of a network and enhances the initial DBA algorithm's stability. The experimental results show that the proposed clique-DBA algorithm converges to a determined local influence estimation (LIE) value in each run, eliminating the phenomenon of large fluctuation of LIE fitness value generated by the original DBA algorithm. Finally, the simulated results achieved under the independent cascade model show that the clique-DBA algorithm has a comparable performance of influence spreading compared with the algorithms proposed in the state of the art.
WOS研究方向	Computer Science
语种	英语
出版者	SPRINGER
WOS记录号	WOS:000636628100001
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/148267]
专题	计算机与通信学院
作者单位	1.Lanzhou Univ Technol, Sch Comp & Commun, Lanzhou, Peoples R China; 2.Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou, Peoples R China; 3.Providence Univ, Dept Comp Sci & Informat Engn, Taichung, Taiwan; 4.Univ Salerno, Dept Comp Sci, Fisciano, SA, Italy; 5.Lanzhou Univ Finance & Econ, Sch Stat, Lanzhou, Peoples R China
推荐引用方式 GB/T 7714	Han, Lihong,Li, Kuan-Ching,Castiglione, Arcangelo,et al. A clique-based discrete bat algorithm for influence maximization in identifying top-k influential nodes of social networks[J]. SOFT COMPUTING,2021,25(13):8223-8240.
APA	Han, Lihong,Li, Kuan-Ching,Castiglione, Arcangelo,Tang, Jianxin,Huang, Hengjun,&Zhou, Qingguo.(2021).A clique-based discrete bat algorithm for influence maximization in identifying top-k influential nodes of social networks.SOFT COMPUTING,25(13),8223-8240.
MLA	Han, Lihong,et al."A clique-based discrete bat algorithm for influence maximization in identifying top-k influential nodes of social networks".SOFT COMPUTING 25.13(2021):8223-8240.