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 101–110 of 712 papers

TitleStatusHype
SGFormer: Semantic Graph Transformer for Point Cloud-based 3D Scene Graph GenerationCode1
Hierarchical Relationships: A New Perspective to Enhance Scene Graph GenerationCode1
Prototype-based Embedding Network for Scene Graph GenerationCode1
RAF: Holistic Compilation for Deep Learning Model TrainingCode1
LANDMARK: Language-guided Representation Enhancement Framework for Scene Graph GenerationCode1
Graph Generation with Diffusion MixtureCode1
GraphGUIDE: interpretable and controllable conditional graph generation with discrete Bernoulli diffusionCode1
Graph Neural Networks can Recover the Hidden Features Solely from the Graph StructureCode1
Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph GenerationCode1
Learning To Generate Language-Supervised and Open-Vocabulary Scene Graph Using Pre-Trained Visual-Semantic SpaceCode1
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Benchmark Results

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