SIMILARITY INDEX BASED ON THE INFORMATION OF NEIGHBOR NODES FOR LINK PREDICTION OF COMPLEX NETWORK

被引:14
作者
Wang, Jing [1 ]
Rong, Lili [1 ]
机构
[1] Dalian Univ Technol, Inst Syst Engn, Dalian 116024, Peoples R China
来源
MODERN PHYSICS LETTERS B | 2013年 / 27卷 / 06期
基金
中国国家自然科学基金;
关键词
Link prediction; complex network; similarity index; GRAPH;
D O I
10.1142/S0217984913500395
中图分类号
O59 [应用物理学];
学科分类号
摘要
Link prediction in complex networks has attracted much attention recently. Many local similarity measures based on the measurements of node similarity have been proposed. Among these local similarity indices, the neighborhood-based indices Common Neighbors (CN), Adamic-Adar (AA) and Resource Allocation (RA) index perform best. It is found that the node similarity indices required only information on the nearest neighbors are assigned high scores and have very low computational complexity. In this paper, a new index based on the contribution of common neighbor nodes to edges is proposed and shown to have competitively good or even better prediction than other neighborhood-based indices especially for the network with low clustering coefficient with its high efficiency and simplicity.
引用
收藏
页数:10
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