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 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.

An adaptive differential evolution algorithm driven by multiple probabilistic mutation strategies for influence maximization in social networks

“…A discrete crow search algorithm was presented by Li et al [35], taking advantage of the crow's tendency to stash extra food away in a secret location and retrieve it when required. Recently, Tang et al [36] presented a learning-automata-driven discrete butterfly optimization algorithm, which further improves the accuracy of IM compared to some state-of-the-art algorithms and achieves a good balance between efficiency and effectiveness. This class of approaches perform satisfying efficiency of the solution to influential maximization by optimizing an evaluation model.…”

A new technique for influence maximization on social networks using a moth-flame optimization algorithm

“…As an illustration, Tang, Zhu [ 43 ] introduced an efficacious solution to the influence maximization problem, employing a novel approach known as the Learning-Automata-Driven Discrete Butterfly Optimization Algorithm (LA-DBOA), meticulously tailored to the network's topology. The empirical outcomes of their work unveiled that the proposed algorithm not only achieved a comparable influence spread to that of the CELF algorithm but also outperformed various conventional methodologies.…”