Identifying influential spreaders in social networks: A two-stage quantum-behaved particle swarm optimization with Lévy flight

Abstract

The influence maximization problem aims to select a small set of influential nodes, termed as seed set, to maximize their influence coverage in the social networks. Although the methods based on greedy strategy can obtain good accuracy, they come at the cost of enormous computational time, are therefore not applicable to practical scenarios on large-scale networks. In addition, the centrality heuristic algorithms based on network topology can be completed in a relatively less time. However, they tend to fail to achieve satisfactory results because of the drawbacks such as overlapped influence spread. In this work, we propose a discrete two-stage metaheuristic optimization combining quantum-behaved particle swarm optimization with Lévy flight for identifying a set of most influential spreaders. According to the framework, firstly, the particles in the population are tasked to conduct exploration in the global solution space to eventually converge to an acceptable solution through the crossover and replacement operations. Secondly, Lévy flight mechanism is used to perform a wandering walk on the optimal candidate solution in the population to exploit the potential unidentified influential nodes in the network. Experiments on six real-world social networks show that the proposed algorithm achieves more satisfactory results compared to other well-known algorithms.