{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T12:11:00Z","timestamp":1776082260347,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,31]],"date-time":"2022-12-31T00:00:00Z","timestamp":1672444800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["LQ20F020011"],"award-info":[{"award-number":["LQ20F020011"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["62162040"],"award-info":[{"award-number":["62162040"]}]},{"name":"National Natural Science Foundations of China","award":["LQ20F020011"],"award-info":[{"award-number":["LQ20F020011"]}]},{"name":"National Natural Science Foundations of China","award":["62162040"],"award-info":[{"award-number":["62162040"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Influence maximization aims at the identification of a small group of individuals that may result in the most wide information transmission in social networks. Although greedy-based algorithms can yield reliable solutions, the computational cost is extreme expensive, especially in large-scale networks. Additionally, centrality-based heuristics tend to suffer from the problem of low accuracy. To solve the influence maximization problem in an efficient way, a learning-automata-driven discrete butterfly optimization algorithm (LA-DBOA) mapped into the network topology is proposed in this paper. According to the LA-DBOA framework, a novel encoding mechanism and discrete evolution rules adapted to network topology are presented. By exploiting the asymmetry of social connections, a modified learning automata is adopted to guide the butterfly population toward promising areas. Based on the topological features of the discrete networks, a new local search strategy is conceived to enhance the search performance of the butterflies. Extensive experiments are conducted on six real networks under the independent cascade model; the results demonstrate that the proposed algorithm achieves comparable influence spread to that of CELF and outperforms other classical methods, which proves that the meta-heuristics based on swarm intelligence are effective in solving the influence maximization problem.<\/jats:p>","DOI":"10.3390\/sym15010117","type":"journal-article","created":{"date-parts":[[2023,1,2]],"date-time":"2023-01-02T02:12:48Z","timestamp":1672625568000},"page":"117","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Maximizing the Influence Spread in Social Networks: A Learning-Automata-Driven Discrete Butterfly Optimization Algorithm"],"prefix":"10.3390","volume":"15","author":[{"given":"Jianxin","family":"Tang","sequence":"first","affiliation":[{"name":"Wenzhou Engineering Institute of Pump & Valve, Lanzhou University of Technology, Wenzhou 325100, China"},{"name":"School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongyu","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jimao","family":"Lan","sequence":"additional","affiliation":[{"name":"School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shihui","family":"Song","sequence":"additional","affiliation":[{"name":"School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1719","DOI":"10.1080\/13658816.2022.2055037","article-title":"Dynamical community detection and spatiotemporal analysis in multilayer spatial interaction networks using trajectory data","volume":"36","author":"Jia","year":"2022","journal-title":"Int. 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