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 661–670 of 712 papers

TitleStatusHype
Disentangling Interpretable Generative Parameters of Random and Real-World Graphs—0
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
Continuous Graph Flow—0
Understanding the Representation Power of Graph Neural Networks in Learning Graph TopologyCode0
The Limited Multi-Label Projection LayerCode0
Learning Predicates as Functions to Enable Few-shot Scene Graph Prediction—0
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Benchmark Results

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