Identification of influential nodes with Shapley Influence Maximization Extremal Optimization algorithm

Abstract

The Influence Maximization Problem is a challenging computational task with multiple real-world applications. A new approach to this problem based on cooperative game theory and optimization called the Shapley Influence Maximization Extremal Optimization approach is proposed. The influence maximization problem for the independent cascade model is considered as a cooperative game, where players seek to choose seeder nodes to maximize the size of the influence set of their cascade model by maximizing their average marginal contribution to all possible coalitions. Numerical experiments conducted on both synthetic and real-world networks and comparisons with state-of-the-art algorithms show the potential of the proposed approach.