A new multi-view graph contrastive learning method generates adaptive views via learnable fractional-order diffusion dynamics instead of manual augmentations and outperforms prior baselines.
Localized contrastive learning on graphs
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2verdicts
UNVERDICTED 2representative citing papers
Message passing trivializes positive sample maximization in GCL via Dirichlet energy smoothing; SPGCL mitigates this by propagating only high-energy features and using low-energy ones for positive sampling.
citing papers explorer
-
Adaptive Multi-view Graph Contrastive Learning via Fractional-order Neural Diffusion Networks
A new multi-view graph contrastive learning method generates adaptive views via learnable fractional-order diffusion dynamics instead of manual augmentations and outperforms prior baselines.