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 131–140 of 712 papers

TitleStatusHype
HL-Net: Heterophily Learning Network for Scene Graph GenerationCode1
RU-Net: Regularized Unrolling Network for Scene Graph GenerationCode1
Graph Pooling for Graph Neural Networks: Progress, Challenges, and OpportunitiesCode1
Explanation Graph Generation via Pre-trained Language Models: An Empirical Study with Contrastive LearningCode1
Fine-Grained Predicates Learning for Scene Graph GenerationCode1
SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph GeneratorsCode1
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
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Benchmark Results

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