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 431–440 of 712 papers

TitleStatusHype
Doubly Reparameterized Importance Weighted Structure Learning for Scene Graph Generation—0
BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed GraphsCode0
Robust Attack Graph Generation—0
The Devil is in the Labels: Noisy Label Correction for Robust Scene Graph GenerationCode1
An Unpooling Layer for Graph GenerationCode0
Expressive Scene Graph Generation Using Commonsense Knowledge Infusion for Visual Understanding and ReasoningCode1
MolScribe: Robust Molecular Structure Recognition with Image-To-Graph GenerationCode2
Temporal Domain Generalization with Drift-Aware Dynamic Neural NetworksCode1
GraphMapper: Efficient Visual Navigation by Scene Graph Generation—0
Importance Weighted Structure Learning 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