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 426–450 of 712 papers

TitleStatusHype
Graph Generative Model for Benchmarking Graph Neural NetworksCode1
GEMS: Scene Expansion using Generative Models of Graphs—0
Unsupervised Knowledge Graph Generation Using Semantic Similarity MatchingCode0
Privacy-preserving Graph Analytics: Secure Generation and Federated Learning—0
Learning To Generate Scene Graph from Head to Tail—0
Doubly Reparameterized Importance Weighted Structure Learning for Scene Graph Generation—0
BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed GraphsCode0
Robust Attack Graph Generation—0
The Devil is in the Labels: Noisy Label Correction for Robust Scene Graph GenerationCode1
An Unpooling Layer for Graph GenerationCode0
Expressive Scene Graph Generation Using Commonsense Knowledge Infusion for Visual Understanding and ReasoningCode1
MolScribe: Robust Molecular Structure Recognition with Image-To-Graph GenerationCode2
Temporal Domain Generalization with Drift-Aware Dynamic Neural NetworksCode1
GraphMapper: Efficient Visual Navigation by Scene Graph Generation—0
Importance Weighted Structure Learning for Scene Graph Generation—0
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