通过可学习的负样本度量空间，提升图对比学习对错误负样本的区分能力。
Negative Metric Learning for Graphs
- 引入可学习的负样本度量网络，在距离空间中区分真假负样本。
- 在多个基准数据集上，下游任务性能显著优于现有方法。
- 无需人工先验知识，通过自监督联合优化实现端到端训练。
图对比学习（GCL）常受错误负样本影响，导致下游任务性能下降。现有方法依赖人工先验知识，仍难以获得最优结果。本文提出一种新型的负样本度量学习增强图对比学习（NML-GCL）。该方法引入可学习的负样本度量网络（NMN），构建负样本度量空间，依据节点与锚点的距离更有效地区分错误负样本与真实负样本。为解决负样本度量学习缺乏显式监督信号的问题，提出双层优化的联合训练策略，隐式利用自监督信号迭代优化编码器与负样本度量网络。理论分析和广泛实验验证了该方法的有效性，其在多个常用基准数据集上均取得优越性能。
原文摘要 · Abstract (English)
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.
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