Using graphon limits for dense graphs, the authors decompose cross-domain output shifts for Lipschitz backbones into graph-specific finite-sample terms and a relabeling-invariant domain discrepancy, with stability results for spectral positional encodings.
Hamilton, Vincent Létourneau, and Prudencio Tossou
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GCCM prevents shortcut collapse in consistency models for graph prediction by using contrastive negative pairs and input feature perturbation, leading to better performance than deterministic baselines.
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When Do Graph Foundation Models Transfer? A Data-Centric Theory
Using graphon limits for dense graphs, the authors decompose cross-domain output shifts for Lipschitz backbones into graph-specific finite-sample terms and a relabeling-invariant domain discrepancy, with stability results for spectral positional encodings.
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GCCM: Enhancing Generative Graph Prediction via Contrastive Consistency Model
GCCM prevents shortcut collapse in consistency models for graph prediction by using contrastive negative pairs and input feature perturbation, leading to better performance than deterministic baselines.