GCCM: Enhancing Generative Graph Prediction via Contrastive Consistency Model
Conditional generative models, particularly diffusion-based methods, have recently been applied to graph prediction by modeling the target as a conditional distribution given the input graph, yielding competitive results compared to deterministic predictor. However, existing diffusion-based prediction methods typically require expensive iterative denoising at inference and often suffer from unstable sampling, which motivates recent efforts to reduce inference denoising steps and enable stable sampling via techniques such as consistency training. Despite this progress, we find that existing consistency training methods for graph prediction could potentially fall into a shortcut solution: t...
| Authors | Shaozhen Ma, Wei Huang, Hanchen Wang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 7 May 2026 |
| Last updated at source | 7 May 2026 |
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