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 151–160 of 712 papers

TitleStatusHype
roadscene2vec: A Tool for Extracting and Embedding Road Scene-GraphsCode1
From General to Specific: Informative Scene Graph Generation via Balance AdjustmentCode1
ReGen: Reinforcement Learning for Text and Knowledge Base Generation using Pretrained Language ModelsCode1
Target Adaptive Context Aggregation for Video Scene Graph GenerationCode1
Scenes and Surroundings: Scene Graph Generation using Relation TransformerCode1
Zero-Shot Scene Graph Relation Prediction through Commonsense Knowledge IntegrationCode1
Molecule Generation by Principal Subgraph Mining and AssemblingCode1
Structured Sparse R-CNN for Direct Scene Graph GenerationCode1
Tackling the Challenges in Scene Graph Generation with Local-to-Global InteractionsCode1
Order Matters: Probabilistic Modeling of Node Sequence for Graph GenerationCode1
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Benchmark Results

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