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 701–712 of 712 papers

TitleStatusHype
Semantic Enrichment of the Quantum Cascade Laser Properties in Text- A Knowledge Graph Generation ApproachCode0
Semantic Parsing via Staged Query Graph Generation: Question Answering with Knowledge BaseCode0
The unknown knowns: a graph-based approach for temporal COVID-19 literature miningCode0
An Unpooling Layer for Graph GenerationCode0
An Equivariant Generative Framework for Molecular Graph-Structure Co-DesignCode0
Sequence-to-Action: End-to-End Semantic Graph Generation for Semantic ParsingCode0
Set-Aligning Framework for Auto-Regressive Event Temporal Graph GenerationCode0
SE-VGAE: Unsupervised Disentangled Representation Learning for Interpretable Architectural Layout Design Graph GenerationCode0
SGDraw: Scene Graph Drawing Interface Using Object-Oriented RepresentationCode0
Connector 0.5: A unified framework for graph representation learningCode0
Top-N: Equivariant set and graph generation without exchangeabilityCode0
GNN-CNN: An Efficient Hybrid Model of Convolutional and Graph Neural Networks for Text RepresentationCode0
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Benchmark Results

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