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 471–480 of 712 papers

TitleStatusHype
GraphTune: A Learning-based Graph Generative Model with Tunable Structural FeaturesCode0
Constrained Structure Learning for Scene Graph Generation—0
RelTR: Relation Transformer for Scene Graph GenerationCode2
Balanced Graph Structure Learning for Multivariate Time Series ForecastingCode0
Resistance Training using Prior Bias: toward Unbiased Scene Graph GenerationCode1
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
SGTR: End-to-end Scene Graph Generation with TransformerCode1
Exploiting Long-Term Dependencies for Generating Dynamic Scene GraphsCode0
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Benchmark Results

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