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Decomposition algorithm of center node in directed-weighted network

Liu Chen
Li Dandan
Han Lin
An Yongxue
Business School, University of Shanghai for Science & Technology, Shanghai 200093, China

Abstract

The existing evaluation methods for node importance in complex network mainly focus on un-directed and un-weighed networks, and can't describe the real-world network completely. Most centrality measures only consider a single indicator, ignoring the difference between out-degree and in-degree of the node, and neglecting the importance of weight. Based on the directed-weighted complex network, this paper proposed a center node recognition algorithm, cw-shell decomposition method, which was based on out-degree, in-degree and weight, considering the difference between out-degree and in-degree of the node, and the practical importance of weight in real network. In order to verify the effectiveness of the new index, it used the weighted-susceptible-infectious-recovered model to simulate spreading process on real-world networks. The results show that the cw-shell decomposition method can rank the nodes efficiently, and identify the nodes with higher diffusion ability.

Foundation Support

国家自然科学基金资助项目(71401107)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2017.06.0648
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 1
Section: Algorithm Research & Explore
Pages: 37-40
Serial Number: 1001-3695(2019)01-008-0037-04

Publish History

[2019-01-05] Printed Article

Cite This Article

刘臣, 李丹丹, 韩林, 等. 加权有向网络中心节点识别的分解算法研究 [J]. 计算机应用研究, 2019, 36 (1): 37-40. (Liu Chen, Li Dandan, Han Lin, et al. Decomposition algorithm of center node in directed-weighted network [J]. Application Research of Computers, 2019, 36 (1): 37-40. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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