A Memetic Algorithm to solve the Robust Influence Maximization Problems against Cascading Failures

Abstract

In complex network systems, the problem that how to select members with considerable information-spreading ability, i.e., the influence maximization (IM) problem, is a current research hotspot. In practice, networked systems are extremely vulnerable to interferences from external sources or even human sabotages, which cause direct disturbances on the topology. One of the common attacks is cascading failures. To cope with the IM problem under cascading failures, a new metric RS-cf is defined to evaluate the performance of seeds under this attack model. Guided by this, a Memetic algorithm, named MA-RIMcf, is devised to determine those nodes with both robustness and influential ability. The reasonableness and effectiveness of the algorithm are verified by experiments on synthetic network data. These solutions are expected to solve the influence maximization problem in realistic environments.