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 181–190 of 712 papers

TitleStatusHype
Sketching Image Gist: Human-Mimetic Hierarchical Scene Graph GenerationCode1
Generative Compositional Augmentations for Scene Graph PredictionCode1
Learning and Reasoning with the Graph Structure Representation in Robotic SurgeryCode1
Adaptive Graph Convolutional Recurrent Network for Traffic ForecastingCode1
GPT-GNN: Generative Pre-Training of Graph Neural NetworksCode1
MoFlow: An Invertible Flow Model for Generating Molecular GraphsCode1
Learning Visual Commonsense for Robust Scene Graph GenerationCode1
CycleGT: Unsupervised Graph-to-Text and Text-to-Graph Generation via Cycle TrainingCode1
XGNN: Towards Model-Level Explanations of Graph Neural NetworksCode1
Graph Density-Aware Losses for Novel Compositions in Scene Graph GenerationCode1
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Benchmark Results

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