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

被引:2
|
作者
Lu, Pengli [2 ]
Lan, Jimao [2 ]
Tang, Jianxin [1 ,2 ]
Zhang, Li [2 ]
Song, Shihui [2 ]
Zhu, Hongyu [2 ]
机构
[1] Lanzhou Univ Technol, Wenzhou Engn Inst Pump&Valve, Wenzhou 325100, Peoples R China
[2] Lanzhou Univ Technol, Sch Comp & Commun, Lanzhou 730050, Peoples R China
基金
中国国家自然科学基金;
关键词
social networks; influence maximization; metaheuristic optimization; quantum-behaved particle swarm optimization; Levy flight; 89.75.-k; 01.75.+m; 03.67.Ac; 05.40.Fb; INFLUENCE MAXIMIZATION; ALGORITHM;
D O I
10.1088/1674-1056/acd3e0
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
The influence maximization problem aims to select a small set of influential nodes, termed a seed set, to maximize their influence coverage in social networks. Although the methods that are based on a greedy strategy can obtain good accuracy, they come at the cost of enormous computational time, and are therefore not applicable to practical scenarios in large-scale networks. In addition, the centrality heuristic algorithms that are based on network topology can be completed in relatively less time. However, they tend to fail to achieve satisfactory results because of drawbacks such as overlapped influence spread. In this work, we propose a discrete two-stage metaheuristic optimization combining quantum-behaved particle swarm optimization with Levy flight to identify a set of the most influential spreaders. According to the framework, first, the particles in the population are tasked to conduct an exploration in the global solution space to eventually converge to an acceptable solution through the crossover and replacement operations. Second, the Levy flight mechanism is used to perform a wandering walk on the optimal candidate solution in the population to exploit the potentially unidentified influential nodes in the network. Experiments on six real-world social networks show that the proposed algorithm achieves more satisfactory results when compared to other well-known algorithms.
引用
收藏
页数:12
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