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 121–130 of 712 papers

TitleStatusHype
AutoKG: Efficient Automated Knowledge Graph Generation for Language ModelsCode1
GraphGUIDE: interpretable and controllable conditional graph generation with discrete Bernoulli diffusionCode1
HL-Net: Heterophily Learning Network for Scene Graph GenerationCode1
Dense Relational Image Captioning via Multi-task Triple-Stream NetworksCode1
Developing a Scalable Benchmark for Assessing Large Language Models in Knowledge Graph EngineeringCode1
Graph Neural Networks can Recover the Hidden Features Solely from the Graph StructureCode1
HOG-Diff: Higher-Order Guided Diffusion for Graph GenerationCode1
Efficient Initial Pose-graph Generation for Global SfMCode1
Grounding Consistency: Distilling Spatial Common Sense for Precise Visual Relationship DetectionCode1
Accurate Learning of Graph Representations with Graph Multiset PoolingCode1
Show:102550
← PrevPage 13 of 72Next →

Benchmark Results

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