Maximizing the influence spread in social networks: a learning automata driven discrete butterfly optimization algorithm

Abstract

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.