期刊
INFORMATION SCIENCES
卷 367, 期 -, 页码 600-614出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2016.07.012
关键词
Social networks; Influence maximization; Cascade model; Particle swarm optimization
资金
- National Natural Science Foundation of China [61273317, 61422209, 61473215]
- National Program for Support of Top-notch Young Professionals of China
- Specialized Research Fund for the Doctoral Program of Higher Education [20130203110011]
Influence maximization in social networks aims to find a small group of individuals, which have maximal influence cascades. In this study, an optimization model based on a local influence criterion is established for the influence maximization problem. The local influence criterion can provide a reliable estimation for the influence propagations in independent and weighted cascade models. A discrete particle swarm optimization algorithm is then proposed to optimize the local influence criterion. The representations and update rules for the particles are redefined in the proposed algorithm. Moreover, a degree based heuristic initialization strategy and a network-specific local search strategy are introduced to speed up the convergence. Experimental results on four real-world social networks demonstrate the effectiveness and efficiency of the proposed algorithm for influence maximization. (C) 2016 Elsevier Inc. All rights reserved.
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