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 441–450 of 712 papers

TitleStatusHype
HL-Net: Heterophily Learning Network for Scene Graph GenerationCode1
RU-Net: Regularized Unrolling Network for Scene Graph GenerationCode1
Learning Reasoning Patterns for Relational Triple Extraction with Mutual Generation of Text and Graph—0
Supplementing Missing Visions via Dialog for Scene Graph GenerationsCode0
Semantic Structure based Query Graph Prediction for Question Answering over Knowledge Graph—0
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
Synthetic Graph Generation to Benchmark Graph Learning—0
SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph GeneratorsCode1
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Benchmark Results

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