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A Novel Multi-Indicator Evaluation Algorithm for Identifying the Important Nodes in Complex Networks

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成果类型:
期刊论文
作者:
Fang Hu;Yuhua Liu;Jianzhi Jin
通讯作者:
Liu, Yuhua(yhliu@mail.ccnu.edu.cn)
作者机构:
[Fang Hu; Yuhua Liu; Jianzhi Jin] School of Computer Science, Central China Normal University, Wuhan, China
[Fang Hu] College of Information Engineering, Hubei University of Chinese Medicine, Wuhan, China
通讯机构:
[Fang Hu; Yuhua Liu*] S
School of Computer Science, Central China Normal University, Wuhan, 430079, China<&wdkj&>College of Information Engineering, Hubei University of Chinese Medicine, Wuhan 430065, China<&wdkj&>School of Computer Science, Central China Normal University, Wuhan, 430079, China
语种:
英文
关键词:
Algorithms;Closeness centralities;Comprehensive analysis;Eigenvector centralities;Important node;Indicator evaluations;Locally linear embedding;Multiple indicators;Real-world networks;Complex networks
期刊:
Journal of Algorithms & Computational Technology
ISSN:
1748-3018
年:
2015
卷:
9
期:
4
页码:
427-448
机构署名:
本校为第一且通讯机构
院系归属:
计算机学院
摘要:
Identification of important nodes is an emerging hot topic in complex networks over the last few years. Various measures have been proposed to characterize the importance of nodes in complex networks, such as the degree, betweenness, closeness, etc. At present, most algorithms of important node evaluation are based on the single-indicator, which can't reflect the whole condition of the complex network. Therefore, in this paper, after choosing multiple indicators from degree centrality, closeness centrality, eigenvector centrality, information centrality, density/clustering coefficient, mutual-...

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