RSDNE: Exploring Relaxed Similarity and Dissimilarity from Completely-Imbalanced Labels for Network Embedding

Authors

  • Zheng Wang Tsinghua University
  • Xiaojun Ye Tsinghua University
  • Chaokun Wang Tsinghua University
  • Yuexin Wu Tsinghua University
  • Changping Wang Tsinghua University
  • Kaiwen Liang Tsinghua University

Keywords:

network embedding, social networks, artificial intelligence

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 a novel semi-supervised network embedding method, termed Relaxed Similarity and Dissimilarity Network Embedding (RSDNE). Specifically, to benefit from the completely-imbalanced labels, RSDNE guarantees both intra-class similarity and inter-class dissimilarity in an approximate way. Experimental results on several real-world datasets demonstrate the superiority of the proposed method.

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Published

2018-04-25

How to Cite

Wang, Z., Ye, X., Wang, C., Wu, Y., Wang, C., & Liang, K. (2018). RSDNE: Exploring Relaxed Similarity and Dissimilarity from Completely-Imbalanced Labels for Network Embedding. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11242