Link prediction based on local community properties

被引：8

作者：

Yang, Xu-Hua ^{[1
]}

Zhang, Hai-Feng ^{[1
]}

Ling, Fei ^{[1
]}

Cheng, Zhi ^{[1
]}

Weng, Guo-Qing ^{[1
]}

Huang, Yu-Jiao ^{[1
]}

机构：

[1] Zhejiang Univ Technol, Coll Comp Sci & Technol, Hangzhou 310023, Zhejiang, Peoples R China

来源：

INTERNATIONAL JOURNAL OF MODERN PHYSICS B | 2016年 / 30卷 / 31期

基金：

中国国家自然科学基金;

关键词：

Complex networks; link prediction; similarity index; local community; SOCIAL NETWORKS;

D O I：

10.1142/S0217979216502222

中图分类号：

O59 [应用物理学];

学科分类号：

摘要：

The link prediction algorithm is one of the key technologies to reveal the inherent rule of network evolution. This paper proposes a novel link prediction algorithm based on the properties of the local community, which is composed of the common neighbor nodes of any two nodes in the network and the links between these nodes. By referring to the node degree and the condition of assortativity or disassortativity in a network, we comprehensively consider the effect of the shortest path and edge clustering coefficient within the local community on node similarity. We numerically show the proposed method provide good link prediction results.

引用

页数：12

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