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 631–640 of 712 papers

TitleStatusHype
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
Learning to Generate Time Series Conditioned Graphs with Generative Adversarial Nets—0
Permutation Invariant Graph Generation via Score-Based Generative ModelingCode1
Unbiased Scene Graph Generation from Biased TrainingCode2
Graph Deconvolutional Generation—0
Hierarchical Generation of Molecular Graphs using Structural MotifsCode1
From Route Instructions to Landmark Graphs—0
Unbiased Scene Graph Generation via Rich and Fair Semantic Extraction—0
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Benchmark Results

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