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 321–330 of 712 papers

TitleStatusHype
Improving Graph Generation by Restricting Graph BandwidthCode0
Graph Autoencoders with Deconvolutional Networks—0
Devil's on the Edges: Selective Quad Attention for Scene Graph Generation—0
Gransformer: Transformer-based Graph Generation—0
Scalable Generative Models for Graphs with Graph Attention Mechanism—0
Automated Graph Generation at Sentence Level for Reading Comprehension Based on Conceptual Graphs—0
Automated Generation of Precedence Graphs in Digital Value Chains for Automotive Production—0
DEFactor: Differentiable Edge Factorization-based Probabilistic Graph Generation—0
GG-GAN: A Geometric Graph Generative Adversarial Network—0
GeSubNet: Gene Interaction Inference for Disease Subtype Network Generation—0
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Benchmark Results

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