SOTAVerified

Graph Generation

Graph Generation is an important research area with significant applications in drug and material designs.

Source: Graph Deconvolutional Generation

Papers

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Showing 331–340 of 712 papers

TitleStatusHype
Efficient and Degree-Guided Graph Generation via Discrete Diffusion ModelingCode1
Analyzing Hong Kong's Legal Judgments from a Computational Linguistics point-of-view—0
Connector 0.5: A unified framework for graph representation learningCode0
Discovering Graph Generation Algorithms—0
KS-GNNExplainer: Global Model Interpretation Through Instance Explanations On Histopathology images—0
An Equivariant Generative Framework for Molecular Graph-Structure Co-DesignCode0
Devil's on the Edges: Selective Quad Attention for Scene Graph Generation—0
Unbiased Scene Graph Generation in VideosCode1
SPAN: Learning Similarity between Scene Graphs and Images with TransformersCode1
FairGen: Towards Fair Graph Generation—0
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Benchmark Results

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