Extending LINE for Network Embedding With Completely Imbalanced Labels

Zheng Wang, Qiao Wang, Tanjie Zhu and Xiaojun Ye
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Zheng Wang: Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing, China
Qiao Wang: School of Software, Tsinghua University, China
Tanjie Zhu: Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing, China
Xiaojun Ye: School of Software, Tsinghua University, China

International Journal of Data Warehousing and Mining (IJDWM), 2020, vol. 16, issue 3, 20-36

Abstract: Network embedding is a fundamental problem in network research. Semi-supervised network embedding, which benefits from labeled data, has recently attracted considerable interest. However, existing semi-supervised methods would get biased results in the completely-imbalanced label setting where labeled data cannot cover all classes. This article proposes a novel network embedding method which could benefit from completely-imbalanced labels by approximately guaranteeing both intra-class similarity and inter-class dissimilarity. In addition, the authors prove and adopt the matrix factorization form of LINE (a famous network embedding method) as the network structure preserving model. Extensive experiments demonstrate the superiority and robustness of this method.

Date: 2020
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