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Identifying Influential Spreaders in Social Networks Through Discrete Moth-Flame Optimization

Wang, Lu and Ma, Lei and Wang, Chao and Xie, Neng-gang and Koh, Jin Ming and Cheong, Kang Hao (2021) Identifying Influential Spreaders in Social Networks Through Discrete Moth-Flame Optimization. IEEE Transactions on Evolutionary Computation, 25 (6). pp. 1091-1102. ISSN 1089-778X. doi:10.1109/tevc.2021.3081478.

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Influence maximization in a social network refers to the selection of node sets that support the fastest and broadest propagation of information under a chosen transmission model. The efficient identification of such influence-maximizing groups is an active area of research with diverse practical relevance. Greedy-based methods can provide solutions of reliable accuracy, but the computational cost of the required Monte Carlo simulations renders them infeasible for large networks. Meanwhile, although network structure-based centrality methods can be efficient, they typically achieve poor recognition accuracy. Here, we establish an effective influence assessment model based both on the total valuation and variance in valuation of neighbor nodes, motivated by the possibility of unreliable communication channels. We then develop a discrete moth-flame optimization method to search for influence-maximizing node sets, using a local crossover and mutation evolution scheme atop the canonical moth position updates. To accelerate convergence, a search area selection scheme derived from a degree-based heuristic is used. The experimental results on five real-world social networks, comparing our proposed method against several alternatives in the current literature, indicates our approach to be effective and robust in tackling the influence maximization problem.

Item Type:Article
Related URLs:
URLURL TypeDescription
Wang, Chao0000-0002-2226-9575
Xie, Neng-gang0000-0002-1296-1349
Koh, Jin Ming0000-0002-6130-5591
Cheong, Kang Hao0000-0002-4475-5451
Additional Information:© 2021 IEEE. Manuscript received September 8, 2020; revised January 3, 2021 and March 9, 2021; accepted May 2, 2021. Date of publication May 18, 2021; date of current version December 1, 2021. This work was supported in part by the Scientific Research Foundation of Education Department of Anhui Province, China, under Grant KJ2019A0091 and Grant KJ2019ZD09; in part by the Humanities and Social Science Fund of Ministry of Education of China under Grant 19YJAZH098; and in part by the Singapore University of Technology and Design Start-Up Research Grant under Project SRG SCI 2019 142.
Funding AgencyGrant Number
Scientific Research Foundation of Education Department of Anhui ProvinceKJ2019A0091
Scientific Research Foundation of Education Department of Anhui ProvinceKJ2019ZD09
Ministry of Education (China)19YJAZH098
Singapore University of TechnologySRG SCI 2019 142
Subject Keywords:Assessment model, influence maximization, moth-flame optimization (MFO), social networks
Issue or Number:6
Record Number:CaltechAUTHORS:20210601-110216789
Persistent URL:
Official Citation:L. Wang, L. Ma, C. Wang, N. -G. Xie, J. M. Koh and K. H. Cheong, "Identifying Influential Spreaders in Social Networks Through Discrete Moth-Flame Optimization," in IEEE Transactions on Evolutionary Computation, vol. 25, no. 6, pp. 1091-1102, Dec. 2021, doi: 10.1109/TEVC.2021.3081478
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:109324
Deposited By: Tony Diaz
Deposited On:01 Jun 2021 18:09
Last Modified:17 Dec 2021 18:33

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