Identifying Influential Spreaders in Social Networks Through Discrete Moth-Flame Optimization
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 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.
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.Attached Files
Accepted Version - 09434427.pdf
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Additional details
- Eprint ID
- 109324
- DOI
- 10.1109/tevc.2021.3081478
- Resolver ID
- CaltechAUTHORS:20210601-110216789
- KJ2019A0091
- Scientific Research Foundation of Education Department of Anhui Province
- KJ2019ZD09
- Scientific Research Foundation of Education Department of Anhui Province
- 19YJAZH098
- Ministry of Education (China)
- SRG SCI 2019 142
- Singapore University of Technology
- Created
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2021-06-01Created from EPrint's datestamp field
- Updated
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2021-12-17Created from EPrint's last_modified field