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 31–40 of 712 papers

TitleStatusHype
A Graph is Worth K Words: Euclideanizing Graph using Pure TransformerCode1
Adaptive Self-training Framework for Fine-grained Scene Graph GenerationCode1
Developing a Scalable Benchmark for Assessing Large Language Models in Knowledge Graph EngineeringCode1
Are scene graphs good enough to improve Image Captioning?Code1
3D Vessel Graph Generation Using Denoising DiffusionCode1
Dense Relational Image Captioning via Multi-task Triple-Stream NetworksCode1
Discrete-state Continuous-time Diffusion for Graph GenerationCode1
A Fair Ranking and New Model for Panoptic Scene Graph GenerationCode1
Adaptive Graph Convolutional Recurrent Network for Traffic ForecastingCode1
CycleGT: Unsupervised Graph-to-Text and Text-to-Graph Generation via Cycle TrainingCode1
Show:102550
← PrevPage 4 of 72Next →

Benchmark Results

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