基于PU学习的链接预测方法

doi:10.16451/j.cnki.issn1003-6059.201909003

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Abstract In classification-based link prediction methods, it is difficult to choose reliable negative examples to construct the link prediction classifier due to the large-scale and uncertainty of the unknown node pairs. Therefore, a link prediction method based on positive and unlabeled(PU) learning is proposed. Firstly, topological information of node pairs is extracted to construct example sets. Secondly, distribution of candidate negative examples is determined by community structure, and several candidate negative example sets are obtained through multiple under-sampling based on the distribution. Then, the classifiers constructed from multiple negative example sets and positive example sets are integrated to select reliable negative examples. Finally, the link prediction classifier is constructed based on positive examples and reliable negative examples. Experiments on four datasets show that the proposed link prediction method produces better prediction results than other related methods.

Key words： Link Prediction Positive and Unlabeled(PU) Learning Community Structure Ensemble Learning

Received: 13 May 2019

ZTFLH:

TP 391

Fund:Supported by National Natural Science Foundation of China(No.61876103, 61906111), Science and Technological Innovation Programs of Higher Education Institutions in Shanxi(No.2019L0023), 1331 Engineering Project of Shanxi Province

Corresponding Authors: LIANG Jiye, Ph.D., professor. His research interests include artificial intelligence, granular computing, data mining and machine learning.

About author:: LI Qi, master student. Her research interests include social network analysis;WANG Zhiqiang, Ph.D., lecturer. His research interests include social network analysis and machine learning.

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Cite this article:

LI Qi,WANG Zhiqiang,LIANG Jiye. Link Prediction Method Based on PU Learning[J]. , 2019, 32(9): 793-799.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201909003 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2019/V32/I9/793

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