Boosting Graph Contrastive Learning via Manifold-Guided Representation Disentanglement

Graph contrastive learning constructs augmented views and aligns corresponding instances across views to learn view-invariant representations. However, existing methods usually enforce cross-view consistency within a single representation space, lacking an explicit characterization of view-specific information. This may suppress task-relevant complementary information, reduce embedding diversity, and induce dimensional collapse. To this end, we propose Manifold-Guided Representation Disentanglement (MGRD), a plug-and-play framework for boosting graph contrastive learning. MGRD decomposes graph representations into shared and complementary subspaces. The shared subspace captures view-invariant semantics while preserving local graph structure through a graph-anchored manifold prior, whereas the complementary subspace captures residual view-specific information beyond the shared representation and is regularized by asymmetric consistency and subspace decorrelation. By jointly exploiting shared and complementary representations, MGRD balances cross-view invariance and representation diversity. Experiments on node-level and graph-level benchmarks show that MGRD consistently improves multiple GCL backbones, while theoretical and empirical analyses demonstrate its effectiveness in mitigating dimensional collapse.