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 71–80 of 712 papers

TitleStatusHype
AutoKG: Efficient Automated Knowledge Graph Generation for Language ModelsCode1
Expanding Scene Graph Boundaries: Fully Open-vocabulary Scene Graph Generation via Visual-Concept Alignment and RetentionCode1
NeuSyRE: Neuro-Symbolic Visual Understanding and Reasoning Framework based on Scene Graph EnrichmentCode1
Sparse Training of Discrete Diffusion Models for Graph GenerationCode1
GraphMaker: Can Diffusion Models Generate Large Attributed Graphs?Code1
Less is More: Toward Zero-Shot Local Scene Graph Generation via Foundation ModelsCode1
Node-Aligned Graph-to-Graph (NAG2G): Elevating Template-Free Deep Learning Approaches in Single-Step RetrosynthesisCode1
Spatial-Temporal Knowledge-Embedded Transformer for Video Scene Graph GenerationCode1
Zero-Shot Scene Graph Generation via Triplet Calibration and ReductionCode1
Developing a Scalable Benchmark for Assessing Large Language Models in Knowledge Graph EngineeringCode1
Show:102550
← PrevPage 8 of 72Next →

Benchmark Results

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