Combination methods for identifying influential nodes in networks

Chao Gao (), Lu Zhong, Xianghua Li, Zili Zhang and Ning Shi
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Chao Gao: College of Computer and Information Science, Southwest University, Chongqing 400715, P. R. China;
Lu Zhong: College of Computer and Information Science, Southwest University, Chongqing 400715, P. R. China
Xianghua Li: College of Computer and Information Science, Southwest University, Chongqing 400715, P. R. China
Zili Zhang: College of Computer and Information Science, Southwest University, Chongqing 400715, P. R. China;
Ning Shi: College of Computer and Information Science, Southwest University, Chongqing 400715, P. R. China

International Journal of Modern Physics C (IJMPC), 2015, vol. 26, issue 06, 1-20

Abstract: Identifying influential nodes is of theoretical significance in many domains. Although lots of methods have been proposed to solve this problem, their evaluations are under single-source attack in scale-free networks. Meanwhile, some researches have speculated that the combinations of some methods may achieve more optimal results. In order to evaluate this speculation and design a universal strategy suitable for different types of networks under the consideration of multi-source attacks, this paper proposes an attribute fusion method with two independent strategies to reveal the correlation of existing ranking methods and indicators. One is based on feature union (FU) and the other is based on feature ranking (FR). Two different propagation models in the fields of recommendation system and network immunization are used to simulate the efficiency of our proposed method. Experimental results show that our method can enlarge information spreading and restrain virus propagation in the application of recommendation system and network immunization in different types of networks under the condition of multi-source attacks.

Keywords: Complex network; influential nodes; feature fusion; SI model; network immunization (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (3)

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DOI: 10.1142/S0129183115500679

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