提出DGMAE，让图自监督学习更好区分不相似的邻居节点。
Discrepancy-Aware Graph Mask Auto-Encoder
- 在掩码过程中重建邻接节点间的差异信息，增强表示区分度。
- 在17个数据集上显著优于现有方法，尤其在异质图任务中提升明显。
- 适合处理节点间差异大的复杂图结构，如社交网络、生物网络。
掩码图自编码器是一种强大的图自监督训练范式，近期在图表示学习中表现优异。现有方法通常依赖节点上下文信息来恢复被掩码内容，但在异质图（连接节点不相似）上泛化能力差，因仅关注邻域信息而忽略节点间的差异信息，导致节点表示难以区分。本文提出差异感知图掩码自编码器（DGMAE），通过在掩码过程中重构邻近节点的差异信息，获得更具区分性的节点表示。我们在17个广泛使用的基准数据集上进行了大量实验，结果表明，DGMAE能有效在低维空间保留节点差异。此外，DGMAE在节点分类、节点聚类和图分类三项图分析任务中显著优于当前最先进方法，展现出显著优势。DGMAE代码已公开于 https://github.com/zhengziyu77/DGMAE。
原文摘要 · Abstract (English)
Masked Graph Auto-Encoder, a powerful graph self-supervised training paradigm, has recently shown superior performance in graph representation learning. Existing works typically rely on node contextual information to recover the masked information. However, they fail to generalize well to heterophilic graphs where connected nodes may be not similar, because they focus only on capturing the neighborhood information and ignoring the discrepancy information between different nodes, resulting in indistinguishable node representations. In this paper, to address this issue, we propose a Discrepancy-Aware Graph Mask Auto-Encoder (DGMAE). It obtains more distinguishable node representations by reconstructing the discrepancy information of neighboring nodes during the masking process. We conduct extensive experiments on 17 widely-used benchmark datasets. The results show that our DGMAE can effectively preserve the discrepancies of nodes in low-dimensional space. Moreover, DGMAE significantly outperforms state-of-the-art graph self-supervised learning methods on three graph analytic including tasks node classification, node clustering, and graph classification, demonstrating its remarkable superiority. The code of DGMAE is available at https://github.com/zhengziyu77/DGMAE.
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