Graph-PiT adds graph priors and a hierarchical GNN to part-based image synthesis to enforce relational constraints and improve structural coherence over vanilla PiT.
LetD ′ = diag(d ′ i)
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GraphWeave learns graph family patterns via random walk trajectories and reconstructs new graphs through joint optimization, outperforming diffusion baselines on benchmarks for structures like communities and degree distributions while running 10x faster.
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.
citing papers explorer
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Graph-PiT: Enhancing Structural Coherence in Part-Based Image Synthesis via Graph Priors
Graph-PiT adds graph priors and a hierarchical GNN to part-based image synthesis to enforce relational constraints and improve structural coherence over vanilla PiT.
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GraphWeave: Interpretable and Robust Graph Generation via Random Walk Trajectories
GraphWeave learns graph family patterns via random walk trajectories and reconstructs new graphs through joint optimization, outperforming diffusion baselines on benchmarks for structures like communities and degree distributions while running 10x faster.
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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.