Analysis similarity index of link prediction based on multivariate statistics

被引:0
|
作者
Tang, Minghu [1 ,2 ]
Wang, Wenjun [1 ]
机构
[1] Tianjin Univ, Sch Comp Sci & Technol, Tianjin 300072, Peoples R China
[2] Qinghai Nationalities Univ, Sch Comp Sci & Technol, Xining 810007, Qinghai, Peoples R China
来源
INTERNATIONAL JOURNAL OF MODERN PHYSICS B | 2018年 / 32卷 / 28期
基金
中国国家自然科学基金;
关键词
Link prediction; performance evaluation; multivariate statistics; MISSING LINKS; NETWORKS;
D O I
10.1142/S0217979218503162
中图分类号
O59 [应用物理学];
学科分类号
摘要
Link prediction attracts the attention of a large number of researchers due to the extensive application in social and economic fields. Many algorithms have been proposed in recent years. They show good performance because of having own particularly selected networks. However, on the other networks, they do not necessarily have good universality. Moreover, there are no other methods to evaluate the performance of new algorithm except AUC and precision. Therefore, we cannot help questioning this phenomenon. Can it really reflect the performance of an algorithm? Which attributes of a network have great influence on the prediction effect? In this paper, we analyze 21 real networks by multivariate statistical analysis. On the one hand, we find that the heterogeneity of network plays a significant role in the result of link prediction. On the other hand, the selection of network is very essential when verifying the performance of new algorithm. In addition, a nonlinear regression model is produced by analyzing the relationship between network properties and similarity methods. Furthermore, 16 similarity methods are analyzed by means of the AUC. The results show that it is of great significance for the performance of a new algorithm to design the evaluation mechanism of classification.
引用
收藏
页数:18
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