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 31–40 of 712 papers

TitleStatusHype
A Graph is Worth K Words: Euclideanizing Graph using Pure TransformerCode1
Adaptive Self-training Framework for Fine-grained Scene Graph GenerationCode1
3D Vessel Graph Generation Using Denoising DiffusionCode1
CogTree: Cognition Tree Loss for Unbiased Scene Graph GenerationCode1
Diffusion-based Graph Generative MethodsCode1
Discrete-state Continuous-time Diffusion for Graph GenerationCode1
A Fair Ranking and New Model for Panoptic Scene Graph GenerationCode1
Adaptive Graph Convolutional Recurrent Network for Traffic ForecastingCode1
CycleGT: Unsupervised Graph-to-Text and Text-to-Graph Generation via Cycle TrainingCode1
Biasing Like Human: A Cognitive Bias Framework for Scene Graph GenerationCode1
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Benchmark Results

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