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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 . ISSN 1089-778X. doi:10.1109/tevc.2021.3081478. (In Press) https://resolver.caltech.edu/CaltechAUTHORS:20210601-110216789

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Abstract

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 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. Experimental results on five real-world social networks, comparing our proposed method against several alternatives in current literature, indicates our approach to be effective and robust in tackling the influence maximization problem.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/tevc.2021.3081478DOIArticle
ORCID:
AuthorORCID
Koh, Jin Ming0000-0002-6130-5591
Cheong, Kang Hao0000-0002-4475-5451
Additional Information:© 2021 IEEE. C. Wang and K. H. Cheong are joint Correspnding Authors. Manuscript received TBC; revised TBC. This work was supported by the Scientific Research Foundation of Education Department of Anhui Province, China (Grant No. KJ2019A0091, KJ2019ZD09), Humanities and Social Science Fund of Ministry of Education of China (Grant No. 19YJAZH098) and the Singapore University of Technology and Design Start-up Research Grant (SRG SCI 2019 142).
Funders:
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:Social networks; influence maximization; assessment model; moth-flame optimization
DOI:10.1109/tevc.2021.3081478
Record Number:CaltechAUTHORS:20210601-110216789
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210601-110216789
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:109324
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:01 Jun 2021 18:09
Last Modified:01 Jun 2021 18:09

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