Maximizing the Influence Spread in Social Networks: A Learning-Automata-Driven Discrete Butterfly Optimization Algorithm

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.

Automated summary

This paper proposes a learning-automata-driven discrete butterfly optimization algorithm (LA-DBOA) to efficiently solve the influence maximization problem. By exploiting the asymmetry of social connections and considering the topological features of discrete networks, the proposed algorithm achieves comparable influence spread to that of CELF and outperforms other classical methods, demonstrating the effectiveness of swarm intelligence-based meta-heuristics in solving the influence maximization problem.