A Memetic Algorithm for Finding Robust and Influential Seeds for Networks under Cascading Failures

How to select a set of nodes with the strongest information spreading capability from a complex network is known as the influence maximization problem. Existing research has mostly focused on structurally stable networks, and the impact of network structural changes on the influence diffusion process is yet to be explored. Simultaneously, network systems are inevitably subject to disturbances or even structural damage during operation, such as cascading failures. To address the robust influence maximization (RIM) problem under cascading failures, this paper investigates the RIM problem caused by link attacks leading to cascading failures. A numerical metric is designed to comprehensively assess the robust influence performance of seeds. For the RIM problem, a memetic algorithm with an ecological niche strategy, termed MA-RIMCF-Link, is designed to find seeds with stable influence, and experiments on synthetic and practical net- works validate the competitiveness and effectiveness of MA-RIMCF-Link compared to existing methods.