SOTAVerified

Graph Generation

Graph Generation is an important research area with significant applications in drug and material designs.

Source: Graph Deconvolutional Generation

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 61–70 of 712 papers

TitleStatusHype
Neural Graph Generator: Feature-Conditioned Graph Generation using Latent Diffusion ModelsCode1
Graph Diffusion Policy OptimizationCode1
Pard: Permutation-Invariant Autoregressive Diffusion for Graph GenerationCode1
A Graph is Worth K Words: Euclideanizing Graph using Pure TransformerCode1
Diffusion-based Graph Generative MethodsCode1
Adaptive Self-training Framework for Fine-grained Scene Graph GenerationCode1
Efficient and Scalable Graph Generation through Iterative Local ExpansionCode1
A Simple and Scalable Representation for Graph GenerationCode1
Panoptic Video Scene Graph GenerationCode1
VLPrompt: Vision-Language Prompting for Panoptic Scene Graph GenerationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1RNNStreetMover0.03—Unverified
2GraphRNNStreetMover0.02—Unverified
3GGT without CAStreetMover0.02—Unverified
4GGTStreetMover0.02—Unverified