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 651–660 of 712 papers

TitleStatusHype
Image-Conditioned Graph Generation for Road Network ExtractionCode0
Leveraging Auxiliary Text for Deep Recognition of Unseen Visual Relationships—0
Graph Embedding VAE: A Permutation Invariant Model of Graph Structure—0
Disentangling Interpretable Generative Parameters of Random and Real-World Graphs—0
Efficient Graph Generation with Graph Recurrent Attention NetworksCode1
Graph Generation with Variational Recurrent Neural Network—0
Graph Residual Flow for Molecular Graph Generation—0
GraphNVP: an Invertible Flow-based Model for Generating Molecular Graphs—0
Distributed Training of Embeddings using Graph Analytics—0
TGG: Transferable Graph Generation for Zero-shot and Few-shot LearningCode0
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Benchmark Results

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