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.
@article{arxiv.2007.03545,
title = {Network Embedding with Completely-imbalanced Labels},
author = {Zheng Wang and Xiaojun Ye and Chaokun Wang and Jian Cui and Philip S. Yu},
journal= {arXiv preprint arXiv:2007.03545},
year = {2025}
}
Comments
A preliminary version of this work was accepted in AAAI 2018. This version has been accepted in IEEE Transactions on Knowledge and Data Engineering (TKDE) 2020. Project page: https://zhengwang100.github.io/project/zero_shot_graph_embedding.html