Structural Neglect Matters: Overfitting and Its Mitigation in Graph Contrastive Learning

Graph contrastive learning (GCL) has emerged as an effective framework for unsupervised graph representation learning, typically relying on GNN-based encoders to capture informative representations from both node attributes and graph structure. Despite the common expectation that optimizing contrastive objectives improves the discriminability of representations for downstream tasks, we reveal that excessive optimization of the graph contrastive objective can instead degrade the learned representations. Specifically, as optimization proceeds, the encoder progressively loses structural information, ultimately reducing the discriminability of the resulting representations for downstream tasks. We refer to this phenomenon as contrastive overfitting. Through theoretical analysis and extensive empirical investigation, we further identify the mechanism underlying contrastive overfitting and characterize how excessive contrastive optimization leads to the progressive degradation of structural information. Guided by these findings, we propose Progress–Adaptive REgulation (PARE), a simple yet effective adaptive training strategy that can be integrated into existing GCL methods to mitigate contrastive overfitting. Extensive experiments demonstrate the effectiveness of PARE across various GCL methods.