Multiobjective memetic algorithm for vital nodes identification in complex networks

Juanjuan Luo, Huadong Ma, Dongqing Zhou

Abstract: Vital nodes identification, that is, finding a set of nodes whose absence would cause a collapse of the network, is a significant project in network science. Despite there are a plenty of methods to identify the vital nodes, two major problems still need to be solved, that is how to select these nodes and how to determine the number of them. In this study, we focus on dealing with these two problems via proposing a multiobjective memetic algorithm for vital nodes identification task. First, vital nodes identification task is modeled as a biobjective optimization problem by analyzing the characteristic of vital nodes. Then, a memetic strategy and specific evolutionary operators inspired by multiple centralities are designed to execute local and global search. In addition, a long-tail property is found from the Pareto front of this bi-objective optimization problem, and the simulation results always show an obvious knee region. Hence, an adaptive learning method to determine the size of vital nodes is designed by searching for the knee point. At last, the proposed framework is tested on the scale free networks and real-life networks, and the simulation results validate its effectivity in contrast with the state-of-art greedy methods.