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 141–150 of 712 papers

TitleStatusHype
One-shot Scene Graph GenerationCode1
Score-based Generative Modeling of Graphs via the System of Stochastic Differential EquationsCode1
Resistance Training using Prior Bias: toward Unbiased Scene Graph GenerationCode1
SGTR: End-to-end Scene Graph Generation with TransformerCode1
Outlining and Filling: Hierarchical Query Graph Generation for Answering Complex Questions over Knowledge GraphsCode1
Topic Scene Graph Generation by Attention Distillation from CaptionCode1
GraphGT: Machine Learning Datasets for Graph Generation and TransformationCode1
Formal Query Building with Query Structure Prediction for Complex Question Answering over Knowledge BaseCode1
GeneAnnotator: A Semi-automatic Annotation Tool for Visual Scene GraphCode1
Learning to Generate Scene Graph from Natural Language SupervisionCode1
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Benchmark Results

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