Predicting missing links in directed complex networks: A linear programming method

被引:4
|
作者
Li, Jin-Song [1 ]
Peng, Jian-Hua [1 ]
Liu, Shu-Xin [1 ]
Li, Zhi-Cheng [1 ]
机构
[1] PLA Strateg Support Force Informat Engn Univ, Zhengzhou 450001, Henan, Peoples R China
来源
MODERN PHYSICS LETTERS B | 2020年 / 34卷 / 29期
基金
中国国家自然科学基金;
关键词
Statistical physics; directed complex networks; link prediction; linear programming;
D O I
10.1142/S0217984920503248
中图分类号
O59 [应用物理学];
学科分类号
摘要
Link prediction in complex networks has always been a hot topic in statistical physics, sociology and information science. Since most works focus on undirected networks, how to predict missing links in directed complex networks remains a valuable and challenging problem. Many existing methods fail to differentiate the information provided by links with different orientations, nor do they consider the unequal contributions of neighbors, leading to deficiency in prediction accuracy. In this paper, we propose a novel link prediction method in directed networks. It calculates the contributions of three types of neighbors by solving a simple linear programming problem. Empirical studies on eight real-world networks show that the proposed method performs better under two evaluation metrics in comparison with nine state-of-art benchmarks.
引用
收藏
页数:14
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