Identifying influential nodes based on local dimension

被引：28

作者：

Pu, Jun ^{[1
]}

Chen, Xiaowu ^{[2
]}

Wei, Daijun ^{[1
,3
]}

Liu, Qi ^{[4
,5
]}

Deng, Yong ^{[1
,6
]}

机构：

[1] Southwest Univ, Sch Comp & Informat Sci, Chongqing 400715, Peoples R China

[2] Beihang Univ, Sch Comp Sci, Beijing 100191, Peoples R China

[3] Hubei Univ Nationalities, Sch Sci, Enshi 445000, Peoples R China

[4] Vanderbilt Univ, Sch Med, Ctr Quantitat Sci, Nashville, TN 37232 USA

[5] Vanderbilt Univ, Sch Med, Dept Biomed Informat, Nashville, TN 37232 USA

[6] Vanderbilt Univ, Sch Engn, Nashville, TN 37235 USA

来源：

EPL | 2014年 / 107卷 / 01期

基金：

中国国家自然科学基金; 国家高技术研究发展计划(863计划);

关键词：

COMPLEX NETWORKS; WEIGHTED NETWORKS; COMMUNITY STRUCTURE; INTERNET TOPOLOGY; EPIDEMIC MODELS; LARGE-SCALE; CENTRALITY; SPREADERS; IDENTIFICATION; DIVERSITY;

D O I：

10.1209/0295-5075/107/10010

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

How to identify influential nodes in complex networks is still an open issue. In this paper, we propose a novel method to identify influential nodes based on the local dimension (LD) of each node, where low LD values are suggestive of high influence. Applied to four real networks, our method has been demonstrated to have a comparable ability of identifying influential nodes with other commonly used methods. Furthermore, our method performs much better than the kappa-shell decomposition method, especially in the network with community structure. It can not only identify the influential nodes but also subdivide the nodes in the innermost layers. Copyright (C) EPLA, 2014

引用

页数：6

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