A Multi-Objective Crow Search Algorithm for Influence Maximization in Social Networks

Influence maximization is a key topic of study in social network analysis. It refers to selecting a set of seed users from a social network and maximizing the number of users expected to be affected. Many related research works on the classical influence maximization problem have concentrated on increasing the influence spread, omitting the cost of seed nodes in the diffusion process. In this work, a multi-objective crow search algorithm (MOCSA) is proposed to optimize the problem with maximum influence spread and minimum cost based on a redefined discrete evolutionary scheme. Specifically, the parameter setting based on the dynamic control strategy and the random walk strategy based on black holes are adopted to improve the convergence efficiency of MOCSA. Six real social networks were selected for experiments and analyzed in comparison with other advanced algorithms. The results of experiments indicate that our proposed MOCSA algorithm performs better than the benchmark algorithm in most cases and improves the total objective function value by more than 20%. In addition, the running time of the MOCSA has also been effectively shortened.