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 541–550 of 712 papers

TitleStatusHype
Balanced Graph Structure Learning for Multivariate Time Series ForecastingCode0
Scene Graph Generation: A Comprehensive Survey—0
Dynamic Scene Graph Generation via Anticipatory Pre-Training—0
PPDL: Predicate Probability Distribution Based Loss for Unbiased Scene Graph Generation—0
Exploiting Long-Term Dependencies for Generating Dynamic Scene GraphsCode0
Graph-wise Common Latent Factor Extraction for Unsupervised Graph Representation LearningCode0
Neural Belief Propagation for Scene Graph Generation—0
Joint Modeling of Visual Objects and Relations for Scene Graph Generation—0
Reinforcement Learning Enhanced Explainer for Graph Neural Networks—0
Not All Relations are Equal: Mining Informative Labels for Scene Graph Generation—0
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Benchmark Results

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