An extended clustering method using H-index and minimum distance for searching multiple key spreaders

Pingle Yang, Xin Liu () and Guiqiong Xu
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Pingle Yang: School of Management, Shanghai University, Shanghai 200444, P. R. China†School of Electrical and Information Engineering, Jiangsu University of Science and Technology, Zhangjiagang, Jiangsu 215600, P. R. China
Xin Liu: School of Management, Shanghai University, Shanghai 200444, P. R. China
Guiqiong Xu: School of Management, Shanghai University, Shanghai 200444, P. R. China

International Journal of Modern Physics C (IJMPC), 2019, vol. 30, issue 07, 1-14

Abstract: Identifying multiple key spreaders in a network can effectively control the diffusion of information and optimize the use of available resources, which can be also called the influence maximization problem in sociology domains. In order to maximize collective influence in complex networks, multiple spreaders must have both large single influence and small overlapping influence, but it is rather difficult to satisfy these two conditions simultaneously. In this paper, we try to achieve the best compromise between importance and dispersibility for multiple spreaders through clustering. The cluster centers are surrounded by nodes with lower influence, and the distance among different cluster centers is relatively far. In addition, the initial centers selection directly affects the efficiency of clustering and the realization of global optimization. Consequently, we present an initial centers selection algorithm combining H-index and minimum distance. The experimental results on four actual datasets show that the proposed method has better performance than the traditional benchmark methods in terms of transmission speed, diffusion scale and structural characteristics.

Keywords: Complex networks; multiple spreaders; clustering; H-index; minimum distance (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1142/S0129183119400084

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