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arXiv · 2007.03545

Network Embedding with Completely-imbalanced Labels

Zheng Wang·Xiaojun Ye·Chaokun Wang·Jian Cui·Philip S. Yu
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Abstract

Network embedding, aiming to project a network into a low-dimensional space, is increasingly becoming a focus of network research. Semi-supervised network embedding takes advantage of labeled data, and has shown promising performance. However, existing semi-supervised methods would get unappealing results in the completely-imbalanced label setting where some classes have no labeled nodes at all. To alleviate this, we propose two novel semi-supervised network embedding methods. The first one is a shallow method named RSDNE. Specifically, to benefit from the completely-imbalanced labels, RSDNE guarantees both intra-class similarity and inter-class dissimilarity in an approximate way. The other method is RECT which is a new class of graph neural networks. Different from RSDNE, to benefit from the completely-imbalanced labels, RECT explores the class-semantic knowledge. This enables RECT to handle networks with node features and multi-label setting. Experimental results on several real-world datasets demonstrate the superiority of the proposed methods. Code is available at https://github.com/zhengwang100/RECT.

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Zheng Wang, Xiaojun Ye, Chaokun Wang, Jian Cui, Philip S. Yu. 2020-07-07. Network Embedding with Completely-imbalanced Labels. https://doi.org/10.1109/tkde.2020.2971490

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