A clique-based discrete bat algorithm for influence maximization in identifying top-k influential nodes of social networks
Han, Lihong1,5; Li, Kuan-Ching2; Castiglione, Arcangelo3; Tang, Jianxin4; Huang, Hengjun5; Zhou, Qingguo1
2021-07
发表期刊SOFT COMPUTING
卷号25期号:13页码:8223-8240
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.
关键词Large-scale networks Top-k influential nodes Discrete bat algorithm Clique partition Diffusion of influence
DOI10.1007/s00500-021-05749-7
收录类别EI ; SCOPUS ; SCIE
ISSN1432-7643
语种英语
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications
WOS记录号WOS:000636628100001
出版者SPRINGER
EI入藏号20211510184823
EI主题词Genetic algorithms
原始文献类型Article
EISSN1433-7479
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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http://ir.lzufe.edu.cn/handle/39EH0E1M/30179
专题教务处
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统计与数据科学学院
作者单位1.Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou, Peoples R China;
2.Providence Univ, Dept Comp Sci & Informat Engn, Taichung, Taiwan;
3.Univ Salerno, Dept Comp Sci, Fisciano, SA, Italy;
4.Lanzhou Univ Technol, Sch Comp & Commun, Lanzhou, Peoples R China;
5.Lanzhou Univ Finance & Econ, Sch Stat, Lanzhou, Peoples R China
第一作者单位兰州财经大学
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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.
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