Researchers Challenge Role of Positive Samples in Graph Contrastive Learning, Propose New Method
Researchers have found that Graph Contrastive Learning (GCL) models can achieve competitive performance even without positive samples, challenging a foundational assumption of the paradigm. The study uses Dirichlet energy analysis to show that message passing in graph encoders effectively neutralizes the learning signal from positive samples. The findings motivate a new method, SPGCL, designed to restore the utility of positive samples by selectively propagating high-energy features.
A preprint submitted to arXiv proposes that positive samples — long considered essential to Graph Contrastive Learning — may be largely trivialized by message passing, a core mechanism in graph neural network encoders. Using Dirichlet energy as a theoretical lens, the authors demonstrate that message passing smooths node features in a way that collapses the learning signal that positive samples are meant to provide, explaining why GCL can remain competitive even when positive samples are removed. To address this limitation, the researchers introduce SPGCL, a method that separates features by their Dirichlet energy level: high-energy features are propagated to generate effective learning signals, while low-energy features are used to construct a probability matrix for reliable positive sample selection. Extensive experiments are reported to validate the approach across multiple benchmarks. The work challenges practitioners to reconsider how positive sampling strategies interact with the underlying architecture of graph encoders.
What's missing
It is unclear whether the Dirichlet energy framework generalizes to heterogeneous or dynamic graphs, and the conditions under which removing positive samples still degrades performance are not fully characterized.
What different sources said
- arXiv cs.LGCenter
Revisiting Positive Samples in Graph Contrastive Learning: From the Perspective of Message Passing
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