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
Micro and Macro Level Graph Modeling for Graph Variational Auto-EncodersCode1
Regularized Graph Structure Learning with Semantic Knowledge for Multi-variates Time-Series ForecastingCode1
Face Super-Resolution Using Stochastic Differential EquationsCode1
Integrating Object-aware and Interaction-aware Knowledge for Weakly Supervised Scene Graph GenerationCode1
Dual-branch Hybrid Learning Network for Unbiased Scene Graph GenerationCode1
Unified 2D and 3D Pre-Training of Molecular RepresentationsCode1
Graph Generative Model for Benchmarking Graph Neural NetworksCode1
The Devil is in the Labels: Noisy Label Correction for Robust Scene Graph GenerationCode1
Expressive Scene Graph Generation Using Commonsense Knowledge Infusion for Visual Understanding and ReasoningCode1
Temporal Domain Generalization with Drift-Aware Dynamic Neural NetworksCode1
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Benchmark Results

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