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 61–70 of 712 papers

TitleStatusHype
Fully Convolutional Scene Graph GenerationCode1
Efficient Graph Generation with Graph Recurrent Attention NetworksCode1
Diffusion-based Graph Generative MethodsCode1
Advancing Graph Generation through Beta DiffusionCode1
DIFFVSGG: Diffusion-Driven Online Video Scene Graph GenerationCode1
3M-Diffusion: Latent Multi-Modal Diffusion for Language-Guided Molecular Structure GenerationCode1
GSDiff: Synthesizing Vector Floorplans via Geometry-enhanced Structural Graph GenerationCode1
Biasing Like Human: A Cognitive Bias Framework for Scene Graph GenerationCode1
Dense Relational Image Captioning via Multi-task Triple-Stream NetworksCode1
CycleGT: Unsupervised Graph-to-Text and Text-to-Graph Generation via Cycle TrainingCode1
Show:102550
← PrevPage 7 of 72Next →

Benchmark Results

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