Researchers Challenge Role of Positive Samples in Graph Contrastive Learning, Propose New Method
A new study on arXiv finds that graph contrastive learning models can achieve competitive performance without positive samples, contradicting conventional wisdom in the field. The researchers theoretically explain this phenomenon through Dirichlet energy analysis of message passing mechanisms in graph encoders. They propose SPGCL, a method that selectively uses high-energy features for positive learning and low-energy features for sampling, with experiments showing improved effectiveness.
Researchers have published a preprint challenging a fundamental assumption in graph contrastive learning (GCL), a mainstream approach for training graph encoders. While GCL conventionally relies on maximizing similarity between positive samples, the authors discovered that models can achieve competitive results without this mechanism. Using Dirichlet energy analysis, they theoretically demonstrate that message passing—a core component of graph encoders—trivializes the contribution of positive samples, limiting their learning effectiveness. To address this, they developed SPGCL, which differentiates between high and low Dirichlet energy features: propagating high-energy features to preserve positive learning signals while using low-energy features for constructing reliable positive sample pairs. Extensive experiments validate the approach's effectiveness, suggesting a more nuanced understanding of how positive samples function in graph neural networks.
What's missing
The preprint does not provide information on computational complexity or scalability comparisons between SPGCL and baseline methods, nor does it discuss potential limitations of the Dirichlet energy framework for different types of graph structures or domains.
What different sources said
- arXiv cs.LGCenter
Revisiting Positive Samples in Graph Contrastive Learning: From the Perspective of Message Passing
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