Predicting links based on knowledge dissemination in complex network

被引:18
|
作者
Zhou, Wen [1 ]
Jia, Yifan [1 ]
机构
[1] Shanghai Univ, Sch Comp Engn & Sci, Shanghai 200444, Peoples R China
基金
中国国家自然科学基金;
关键词
Complex network; Network evolving mechanism; Link prediction; H-index; MISSING LINKS; EMERGENCE; INDEX; GRAPH;
D O I
10.1016/j.physa.2016.12.067
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
Link prediction is the task of mining the missing links in networks or predicting the next vertex pair to be connected by a link. A lot of link prediction methods were inspired by evolutionary processes of networks. In this paper, a new mechanism for the formation of complex networks called knowledge dissemination (KD) is proposed with the assumption of knowledge disseminating through the paths of a network. Accordingly, a new link prediction method knowledge dissemination based link prediction (KDLP) is proposed to test MD. KDLP characterizes vertex similarity based on knowledge quantity (KQ) which measures the importance of a vertex through H-index. Extensive numerical simulations on six real-world networks demonstrate that KDLP is a strong link prediction method which performs at a higher prediction accuracy than four well-known similarity measures including common neighbors, local path index, average commute time and matrix forest index. Furthermore, based on the common conclusion that an excellent link prediction method reveals a good evolving mechanism, the experiment results suggest that KD is a considerable network evolving mechanism for the formation of complex networks. (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码:561 / 568
页数:8
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