Less is More: Towards Simple Graph Contrastive Learning

• URL: http://arxiv.org/abs/2509.25742v1
• Date: Tue, 30 Sep 2025 03:56:50 GMT
• Title: Less is More: Towards Simple Graph Contrastive Learning
• Authors: Yanan Zhao, Feng Ji, Jingyang Dai, Jiaze Ma, Wee Peng Tay,
• Abstract summary: Graph Contrastive Learning (GCL) has shown strong promise for unsupervised graph representation learning, yet its effectiveness on heterophilic graphs remains limited.<n>Most existing methods rely on complex augmentation schemes, intricate encoders, or negative sampling.<n>We uncover a simple yet effective principle for GCL: mitigating node feature noise by aggregating it with structural features from the graph topology.
• Score: 28.21633108265473
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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