Proceedings

Negative Metric Learning for Graphs

Yiyang Zhao, Chengpei Wu, Lilin Zhang, Ning Yang

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 7137-7145.

https://doi.org/10.24963/ijcai.2025/794

Keywords

  • Machine Learning: ML: Sequence and graph learning
  • Machine Learning: ML: Self-supervised Learning

Abstract

Graph contrastive learning (GCL) often suffers from false negatives, which degrades the performance on downstream tasks. The existing methods addressing the false negative issue usually rely on human prior knowledge, still leading GCL to suboptimal results. In this paper, we propose a novel Negative Metric Learning (NML) enhanced GCL (NML-GCL). NML-GCL employs a learnable Negative Metric Network (NMN) to build a negative metric space, in which false negatives can be distinguished better from true negatives based on their distance to anchor node. To overcome the lack of explicit supervision signals for NML, we propose a joint training scheme with bi-level optimization objective, which implicitly utilizes the self-supervision signals to iteratively optimize the encoder and the negative metric network. The solid theoretical analysis and the extensive experiments conducted on widely used benchmarks verify the superiority of the proposed method.

Yiyang Zhao, Chengpei Wu, Lilin Zhang, and Ning Yang. 2025. Negative Metric Learning for Graphs. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 7137–7145. https://doi.org/10.24963/ijcai.2025/794
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