SOTAVerified

Graph Generation

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

Source: Graph Deconvolutional Generation

Papers

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Showing 71–80 of 712 papers

TitleStatusHype
Compositional Feature Augmentation for Unbiased Scene Graph GenerationCode1
CycleGT: Unsupervised Graph-to-Text and Text-to-Graph Generation via Cycle TrainingCode1
Combinatorial Complex Score-based Diffusion Modelling through Stochastic Differential EquationsCode1
3M-Diffusion: Latent Multi-Modal Diffusion for Language-Guided Molecular Structure GenerationCode1
Bipartite Graph Network with Adaptive Message Passing for Unbiased Scene Graph GenerationCode1
Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph GenerationCode1
Face Super-Resolution Using Stochastic Differential EquationsCode1
Bridging Knowledge Graphs to Generate Scene GraphsCode1
Fast Graph Generation via Spectral DiffusionCode1
Fine-Grained Evaluation of Large Vision-Language Models in Autonomous DrivingCode1
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Benchmark Results

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