In the rapidly evolving field of self-supervised learning on graphs, generative and contrastive methodologies have emerged as two dominant approaches. Our study focuses on masked feature reconstruction (MFR), a generative technique where a model learns to restore the raw features of masked nodes in a self-supervised manner. We observe that both MFR and graph contrastive learning (GCL) aim to maximize agreement between similar elements. Building on this observation, we reveal a novel theoretical insight: under specific conditions, the objectives of MFR and node-level GCL converge, despite their distinct operational mechanisms. This theoretical connection suggests these approaches are complementary rather than fundamentally different, prompting us to explore their integration to enhance self-supervised learning on graphs. Our research presents Contrastive Masked Feature Reconstruction (CORE), a novel graph self-supervised learning framework that integrates contrastive learning into MFR. Specifically, we form positive pairs exclusively between the original and reconstructed features of masked nodes, encouraging the encoder to prioritize contextual information over the node's own features. Additionally, we leverage the masked nodes themselves as negative samples, combining MFR's reconstructive power with GCL's discriminative ability to better capture intrinsic graph structures. Empirically, our proposed framework CORE significantly outperforms MFR across node and graph classification tasks, demonstrating state-of-the-art results. In particular, CORE surpasses GraphMAE and GraphMAE2 by up to 2.80% and 3.72% on node classification tasks, and by up to 3.82% and 3.76% on graph classification tasks.

翻译：在图自监督学习这一快速发展的领域中，生成式与对比式方法已成为两大主流范式。本研究聚焦于掩码特征重建（MFR），这是一种生成式技术，模型以自监督方式学习恢复被掩码节点的原始特征。我们观察到，MFR与图对比学习（GCL）均致力于最大化相似元素间的一致性。基于这一观察，我们揭示了一个新颖的理论见解：在特定条件下，尽管MFR与节点级GCL的操作机制不同，但二者的目标函数趋于一致。这一理论联系表明，这些方法本质上是互补而非对立的，这促使我们探索将其整合以增强图上的自监督学习。本研究提出了对比掩码特征重建（CORE），一个将对比学习融入MFR的新型图自监督学习框架。具体而言，我们仅在掩码节点的原始特征与重建特征之间构建正样本对，促使编码器更关注上下文信息而非节点自身特征。此外，我们利用掩码节点自身作为负样本，结合MFR的重建能力与GCL的判别能力，以更好地捕捉图的内在结构。实验表明，我们提出的CORE框架在节点分类和图分类任务上均显著优于MFR，取得了最先进的性能。具体来说，在节点分类任务中，CORE相比GraphMAE和GraphMAE2分别提升了最高2.80%和3.72%；在图分类任务中，则分别提升了最高3.82%和3.76%。