SOTAVerified

Graph Generation

Graph Generation is an important research area with significant applications in drug and material designs.

Source: Graph Deconvolutional Generation

Papers

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Showing 121–130 of 712 papers

TitleStatusHype
AutoKG: Efficient Automated Knowledge Graph Generation for Language ModelsCode1
Efficient Initial Pose-graph Generation for Global SfMCode1
EgoExOR: An Ego-Exo-Centric Operating Room Dataset for Surgical Activity UnderstandingCode1
Dense Relational Image Captioning via Multi-task Triple-Stream NetworksCode1
Developing a Scalable Benchmark for Assessing Large Language Models in Knowledge Graph EngineeringCode1
Multimodal Large Language Models for Inverse Molecular Design with Retrosynthetic PlanningCode1
ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense ReasoningCode1
Efficient and Degree-Guided Graph Generation via Discrete Diffusion ModelingCode1
Efficient and Scalable Graph Generation through Iterative Local ExpansionCode1
Accurate Learning of Graph Representations with Graph Multiset PoolingCode1
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Benchmark Results

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