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
Fast Graph Generation via Spectral DiffusionCode1
Context-Aware Scene Graph Generation With Seq2Seq TransformersCode1
Adaptive Self-training Framework for Fine-grained Scene Graph GenerationCode1
Fine-Grained Predicates Learning for Scene Graph GenerationCode1
Dense Relational Image Captioning via Multi-task Triple-Stream NetworksCode1
Fine-Grained Scene Graph Generation with Data TransferCode1
A Graph is Worth K Words: Euclideanizing Graph using Pure TransformerCode1
From General to Specific: Informative Scene Graph Generation via Balance AdjustmentCode1
Graph Density-Aware Losses for Novel Compositions in Scene Graph GenerationCode1
Diffusion-based Graph Generative MethodsCode1
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Benchmark Results

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