Graph Contrastive Learning (GCL) has shown strong promise for unsupervised graph representation learning, yet its effectiveness on heterophilic graphs, where connected nodes often belong to different classes, remains limited. Most existing methods rely on complex augmentation schemes, intricate encoders, or negative sampling, which raises the question of whether such complexity is truly necessary in this challenging setting. In this work, we revisit the foundations of supervised and unsupervised learning on graphs and uncover a simple yet effective principle for GCL: mitigating node feature noise by aggregating it with structural features derived from the graph topology. This observation suggests that the original node features and the graph structure naturally provide two complementary views for contrastive learning. Building on this insight, we propose an embarrassingly simple GCL model that uses a GCN encoder to capture structural features and an MLP encoder to isolate node feature noise. Our design requires neither data augmentation nor negative sampling, yet achieves state-of-the-art results on heterophilic benchmarks with minimal computational and memory overhead, while also offering advantages in homophilic graphs in terms of complexity, scalability, and robustness. We provide theoretical justification for our approach and validate its effectiveness through extensive experiments, including robustness evaluations against both black-box and white-box adversarial attacks.

翻译：图对比学习（GCL）在无监督图表示学习领域展现出强大潜力，但在异配图（其中连接节点常属于不同类别）中的有效性仍受局限。现有方法大多依赖复杂的增强方案、精密的编码器或负采样策略，这引发了一个关键问题：在这种具有挑战性的场景中，此类复杂性是否真正必要？本文重新审视了图上监督与无监督学习的基础原理，揭示出图对比学习的一个简单而有效的原则：通过将节点特征与源自图拓扑的结构特征进行聚合，以缓解节点特征噪声。这一发现表明，原始节点特征与图结构天然为对比学习提供了两个互补视角。基于此见解，我们提出了一个超简化的GCL模型——仅利用GCN编码器捕获结构特征，并以MLP编码器隔离节点特征噪声。该设计既无需数据增强也不需负采样，却在异配基准上以极小的计算和存储开销实现了最先进性能，同时在同配图中展现出复杂度、可扩展性和鲁棒性方面的优势。我们为该方法提供了理论依据，并通过涵盖黑盒与白盒对抗攻击鲁棒性评估在内的大量实验验证了其有效性。