期刊
INFORMATION SCIENCES
卷 278, 期 -, 页码 535-544出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2014.03.070
关键词
Data mining; Social network; Influence maximization; Information propagation; Node centrality
资金
- National Natural Science Foundation major research projects of China [90924029]
- National Natural Science Foundation of China [10761007, 11071108, 11361042]
- National High-tech R&D Program of China [2009AA04Z136]
Information flows in a network where individuals influence each other. In this paper, we study the influence maximization problem of finding a small subset of nodes in a social network that could maximize the spread of influence. We propose a novel information diffusion model CTMC-ICM, which introduces the theory of Continuous-Time Markov Chain (CTMC) into the Independent Cascade Model (ICM). Furthermore, we propose a new ranking metric named SpreadRank generalized by the new information propagation model CTMC-ICM. We experimentally demonstrate the new ranking method that can, in general, extract nontrivial nodes as an influential node set that maximizes the spread of information in a social network and is more efficient than a distance-based centrality. (C) 2014 Elsevier Inc. All rights reserved.
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