SOTAVerified

Graph Generation

Graph Generation is an important research area with significant applications in drug and material designs.

Source: Graph Deconvolutional Generation

Papers

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Showing 151–175 of 712 papers

TitleStatusHype
Explanation Graph Generation via Pre-trained Language Models: An Empirical Study with Contrastive LearningCode1
Bipartite Graph Network with Adaptive Message Passing for Unbiased Scene Graph GenerationCode1
Diffusion-based Graph Generative MethodsCode1
Graph Neural Networks can Recover the Hidden Features Solely from the Graph StructureCode1
Exploiting Edge-Oriented Reasoning for 3D Point-based Scene Graph AnalysisCode1
Graph R-CNN for Scene Graph GenerationCode1
Bridging Knowledge Graphs to Generate Scene GraphsCode1
Hierarchical Generation of Molecular Graphs using Structural MotifsCode1
Autoregressive Diffusion Model for Graph GenerationCode1
Efficient and Degree-Guided Graph Generation via Discrete Diffusion ModelingCode1
Efficient and Scalable Graph Generation through Iterative Local ExpansionCode1
Hyperbolic Graph Diffusion ModelCode1
Efficient Graph Generation with Graph Recurrent Attention NetworksCode1
Efficient Initial Pose-graph Generation for Global SfMCode1
EgoExOR: An Ego-Exo-Centric Operating Room Dataset for Surgical Activity UnderstandingCode1
Expressive Scene Graph Generation Using Commonsense Knowledge Infusion for Visual Understanding and ReasoningCode1
Exploring the Individuality and Collectivity of Intents behind Interactions for Graph Collaborative FilteringCode1
Energy-Based Learning for Scene Graph GenerationCode1
LANDMARK: Language-guided Representation Enhancement Framework for Scene Graph GenerationCode1
Large Language Models as Realistic Microservice Trace GeneratorsCode1
Learning and Reasoning with the Graph Structure Representation in Robotic SurgeryCode1
Learning Joint 2D & 3D Diffusion Models for Complete Molecule GenerationCode1
Fine-Grained Scene Graph Generation with Data TransferCode1
Learning To Generate Language-Supervised and Open-Vocabulary Scene Graph Using Pre-Trained Visual-Semantic SpaceCode1
GPT-GNN: Generative Pre-Training of Graph Neural NetworksCode1
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Benchmark Results

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