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 451–460 of 712 papers

TitleStatusHype
Gransformer: Transformer-based Graph Generation—0
Fine-Grained Scene Graph Generation with Data TransferCode1
Relationformer: A Unified Framework for Image-to-Graph GenerationCode1
Stacked Hybrid-Attention and Group Collaborative Learning for Unbiased Scene Graph GenerationCode1
Biasing Like Human: A Cognitive Bias Framework for Scene Graph GenerationCode1
Hierarchical Memory Learning for Fine-Grained Scene Graph Generation—0
A Survey on Deep Graph Generation: Methods and Applications—0
AgraSSt: Approximate Graph Stein Statistics for Interpretable Assessment of Implicit Graph GeneratorsCode0
Dual Embodied-Symbolic Concept Representations for Deep Learning—0
Disentangled Spatiotemporal Graph Generative Models—0
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Benchmark Results

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