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 51–60 of 712 papers

TitleStatusHype
Adaptive Self-training Framework for Fine-grained Scene Graph GenerationCode1
A Simple and Scalable Representation for Graph GenerationCode1
A Graph is Worth K Words: Euclideanizing Graph using Pure TransformerCode1
Efficient and Scalable Graph Generation through Iterative Local ExpansionCode1
Data Imputation with Iterative Graph ReconstructionCode1
Energy-Based Learning for Scene Graph GenerationCode1
ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense ReasoningCode1
Explanation Graph Generation via Pre-trained Language Models: An Empirical Study with Contrastive LearningCode1
Expressive Scene Graph Generation Using Commonsense Knowledge Infusion for Visual Understanding and ReasoningCode1
Directed Graph Grammars for Sequence-based LearningCode1
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Benchmark Results

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