A Memetic algorithm for determining robust and influential seeds against structural perturbances in competitive networks

Abstract

The influence maximization problem has attracted increasing attention in previous studies. Recent years have witnessed an enormous interest in the modeling, performance evaluation, and seed determination in different networked systems. Further, the competitive behavior between multiple influential groups within a network is developed to simulate the realistic marketing and propagation tasks. Powerful seeds can be detected to achieve considerable diffusion effects. Meanwhile, networks are operated in the presentence of disturbances, and the connectivity tends to be threatened by attacks and errors. Seeds with robustness against structural perturbances in competitive networks are significant for daily applications. However, little attention has been paid on evaluating the robustness of seeds towards competitive spreading scenarios, and an effective determination strategy is still lacked. In order to tackling the robust competitive influence maximization problem, a diffusion model considering competitive behaviors between spreading groups has been developed, and the spreading ability estimation technique is also given. Equipped with which, a robustness measure RCS is designed to evaluate the robustness of seeds under node-based attacks in a numerical form. The seed determination task has been modeled as a discrete optimization problem, and a Memetic algorithm containing several problem-orientated operators, termed MA-RCIM, is devised to solve the problem. Tested on several synthetic and real-world networks, MA-RCIM achieves satisfactory results for solving diffusion dilemmas, and shows superiority over existing approaches.