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 111–120 of 712 papers

TitleStatusHype
Efficient Initial Pose-graph Generation for Global SfMCode1
CycleGT: Unsupervised Graph-to-Text and Text-to-Graph Generation via Cycle TrainingCode1
Junction Tree Variational Autoencoder for Molecular Graph GenerationCode1
Data Imputation with Iterative Graph ReconstructionCode1
LANDMARK: Language-guided Representation Enhancement Framework for Scene Graph GenerationCode1
Large Language Models as Realistic Microservice Trace GeneratorsCode1
LayerDAG: A Layerwise Autoregressive Diffusion Model for Directed Acyclic Graph GenerationCode1
Learning and Reasoning with the Graph Structure Representation in Robotic SurgeryCode1
Learning To Generate Language-Supervised and Open-Vocabulary Scene Graph Using Pre-Trained Visual-Semantic SpaceCode1
Expanding Scene Graph Boundaries: Fully Open-vocabulary Scene Graph Generation via Visual-Concept Alignment and RetentionCode1
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Benchmark Results

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