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 161–170 of 712 papers

TitleStatusHype
Efficient and Scalable Graph Generation through Iterative Local ExpansionCode1
Integrating Object-aware and Interaction-aware Knowledge for Weakly Supervised Scene Graph GenerationCode1
Efficient Graph Generation with Graph Recurrent Attention NetworksCode1
Efficient Initial Pose-graph Generation for Global SfMCode1
EgoExOR: An Ego-Exo-Centric Operating Room Dataset for Surgical Activity UnderstandingCode1
From General to Specific: Informative Scene Graph Generation via Balance AdjustmentCode1
Exploiting Edge-Oriented Reasoning for 3D Point-based Scene Graph AnalysisCode1
Energy-Based Learning for Scene Graph GenerationCode1
Large Language Models for Constrained-Based Causal DiscoveryCode1
Graph Diffusion Policy OptimizationCode1
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Benchmark Results

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