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 26–50 of 712 papers

TitleStatusHype
EgoExOR: An Ego-Exo-Centric Operating Room Dataset for Surgical Activity UnderstandingCode1
Dense Relational Image Captioning via Multi-task Triple-Stream NetworksCode1
Data Imputation with Iterative Graph ReconstructionCode1
Efficient Initial Pose-graph Generation for Global SfMCode1
Energy-Based Learning for Scene Graph GenerationCode1
A Graph is Worth K Words: Euclideanizing Graph using Pure TransformerCode1
Adaptive Self-training Framework for Fine-grained Scene Graph GenerationCode1
Efficient and Scalable Graph Generation through Iterative Local ExpansionCode1
Developing a Scalable Benchmark for Assessing Large Language Models in Knowledge Graph EngineeringCode1
Context-Aware Scene Graph Generation With Seq2Seq TransformersCode1
Efficient and Degree-Guided Graph Generation via Discrete Diffusion ModelingCode1
Efficient Graph Generation with Graph Recurrent Attention NetworksCode1
Expanding Scene Graph Boundaries: Fully Open-vocabulary Scene Graph Generation via Visual-Concept Alignment and RetentionCode1
CogTree: Cognition Tree Loss for Unbiased Scene Graph GenerationCode1
Any-Property-Conditional Molecule Generation with Self-Criticism using Spanning TreesCode1
Combinatorial Complex Score-based Diffusion Modelling through Stochastic Differential EquationsCode1
Discrete-state Continuous-time Diffusion for Graph GenerationCode1
Bridging Knowledge Graphs to Generate Scene GraphsCode1
Adaptive Graph Convolutional Recurrent Network for Traffic ForecastingCode1
A Fair Ranking and New Model for Panoptic Scene Graph GenerationCode1
Are scene graphs good enough to improve Image Captioning?Code1
3D Vessel Graph Generation Using Denoising DiffusionCode1
A Review and Efficient Implementation of Scene Graph Generation MetricsCode1
CycleGT: Unsupervised Graph-to-Text and Text-to-Graph Generation via Cycle TrainingCode1
CARE: Causality Reasoning for Empathetic Responses by Conditional Graph GenerationCode1
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Benchmark Results

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