SOTAVerified

Graph Generation

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

Source: Graph Deconvolutional Generation

Papers

Showing 526550 of 712 papers

TitleStatusHype
Generalized Visual Relation Detection with Diffusion Models0
Generating Triples with Adversarial Networks for Scene Graph Construction0
Generative AI for Visualization: State of the Art and Future Directions0
Generative Explanations for Graph Neural Network: Methods and Evaluations0
Generative Graph Convolutional Network for Growing Graphs0
Reviving Life on the Edge: Joint Score-Based Graph Generation of Rich Edge Attributes0
Generative modeling of the enteric nervous system employing point pattern analysis and graph construction0
GeSubNet: Gene Interaction Inference for Disease Subtype Network Generation0
GG-GAN: A Geometric Graph Generative Adversarial Network0
Scalable Generative Models for Graphs with Graph Attention Mechanism0
Gransformer: Transformer-based Graph Generation0
Graph Autoencoders with Deconvolutional Networks0
Graph-Aware Transformer: Is Attention All Graphs Need?0
Graph Community Augmentation with GMM-based Modeling in Latent Space0
Graph Context Encoder: Graph Feature Inpainting for Graph Generation and Self-supervised Pretraining0
Graph Deconvolutional Generation0
GraphDF: A Discrete Flow Model for Molecular Graph Generation0
GraphEBM: Towards Permutation Invariant and Multi-Objective Molecular Graph Generation0
Graph Embedding VAE: A Permutation Invariant Model of Graph Structure0
Graph Energy-based Model for Molecular Graph Generation0
GraphGAN: Generating Graphs via Random Walks0
Graph Generation via Scattering0
Graph Generation via Spectral Diffusion0
Graph Generation with Variational Recurrent Neural Network0
Graph Generative Pre-trained Transformer0
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Benchmark Results

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