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.

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Influence maximization aims at the identification of a small group of individuals that could result in the most widely transmission of information in social networks. Although greedy-based algorithms can yield reliable solutions, the computational cost is extreme expensive especially in large-scale networks. Meanwhile, the traditional heuristics such as degree and betweenness centralities tend to suffer from the problem of low accuracy. Locating influential nodes effectively using swarm intelligence algorithms remains a research hotspot in social network analysis. 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. A modified learning automata is adopted to guide the butterfly population towards promising areas. Based on the topological characteristics of the discrete network, 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, and the results demonstrate that the proposed algorithm outperforms the state-of-the-art methods when trade-off is made between efficiency and effectiveness.

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