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 41–50 of 712 papers

TitleStatusHype
Neuro-Symbolic Scene Graph Conditioning for Synthetic Image Dataset Generation—0
What can Off-the-Shelves Large Multi-Modal Models do for Dynamic Scene Graph Generation?—0
Universal Scene Graph Generation—0
Conformal Prediction and MLLM aided Uncertainty Quantification in Scene Graph Generation—0
DIFFVSGG: Diffusion-Driven Online Video Scene Graph GenerationCode1
Inductive Spatio-Temporal Kriging with Physics-Guided Increment Training Strategy for Air Quality Inference—0
FunGraph: Functionality Aware 3D Scene Graphs for Language-Prompted Scene Interaction—0
Pretraining Generative Flow Networks with Inexpensive Rewards for Molecular Graph Generation—0
Backdoor Attacks on Discrete Graph Diffusion Models—0
Learning-Order Autoregressive Models with Application to Molecular Graph Generation—0
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Benchmark Results

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