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 81–90 of 712 papers

TitleStatusHype
Context-Aware Scene Graph Generation With Seq2Seq TransformersCode1
CARE: Causality Reasoning for Empathetic Responses by Conditional Graph GenerationCode1
Efficient and Degree-Guided Graph Generation via Discrete Diffusion ModelingCode1
Generative Compositional Augmentations for Scene Graph PredictionCode1
Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph GenerationCode1
Formal Query Building with Query Structure Prediction for Complex Question Answering over Knowledge BaseCode1
Energy-Based Learning for Scene Graph GenerationCode1
Efficient and Scalable Graph Generation through Iterative Local ExpansionCode1
Adaptive Graph Convolutional Recurrent Network for Traffic ForecastingCode1
CycleGT: Unsupervised Graph-to-Text and Text-to-Graph Generation via Cycle TrainingCode1
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Benchmark Results

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