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 191–200 of 712 papers

TitleStatusHype
Secure Deep Graph Generation with Link Differential PrivacyCode1
Relation Transformer NetworkCode1
The general theory of permutation equivarant neural networks and higher order graph variational encodersCode1
GPS-Net: Graph Property Sensing Network for Scene Graph GenerationCode1
Permutation Invariant Graph Generation via Score-Based Generative ModelingCode1
Hierarchical Generation of Molecular Graphs using Structural MotifsCode1
GraphAF: a Flow-based Autoregressive Model for Molecular Graph GenerationCode1
GraphGen: A Scalable Approach to Domain-agnostic Labeled Graph GenerationCode1
NODIS: Neural Ordinary Differential Scene UnderstandingCode1
Weakly Supervised Visual Semantic ParsingCode1
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Benchmark Results

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