1. Identifying influential nodes in complex networks with community structure
- Author
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Zhang, Xiaohang, Zhu, Ji, Wang, Qi, and Zhao, Han
- Subjects
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COMPUTER networks , *COMMUNITY organization , *DECOMPOSITION method , *GREEDY algorithms , *HEURISTIC algorithms , *KNOWLEDGE transfer , *PROBABILITY theory - Abstract
Abstract: It is a fundamental issue to find a small subset of influential individuals in a complex network such that they can spread information to the largest number of nodes in the network. Though some heuristic methods, including degree centrality, betweenness centrality, closeness centrality, the k-shell decomposition method and a greedy algorithm, can help identify influential nodes, they have limitations for networks with community structure. This paper reveals a new measure for assessing the influence effect based on influence scope maximization, which can complement the traditional measure of the expected number of influenced nodes. A novel method for identifying influential nodes in complex networks with community structure is proposed. This method uses the information transfer probability between any pair of nodes and the k-medoid clustering algorithm. The experimental results show that the influential nodes identified by the k-medoid method can influence a larger scope in networks with obvious community structure than the greedy algorithm without reducing the expected number of influenced nodes. [Copyright &y& Elsevier]
- Published
- 2013
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