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 411–420 of 712 papers

TitleStatusHype
Generative Explanations for Graph Neural Network: Methods and Evaluations—0
Generative Graph Convolutional Network for Growing Graphs—0
Reviving Life on the Edge: Joint Score-Based Graph Generation of Rich Edge Attributes—0
Generative modeling of the enteric nervous system employing point pattern analysis and graph construction—0
GeSubNet: Gene Interaction Inference for Disease Subtype Network Generation—0
GG-GAN: A Geometric Graph Generative Adversarial Network—0
Scalable Generative Models for Graphs with Graph Attention Mechanism—0
Gransformer: Transformer-based Graph Generation—0
Graph Autoencoders with Deconvolutional Networks—0
Graph-Aware Transformer: Is Attention All Graphs Need?—0
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Benchmark Results

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