Kernekoncepter
Neural Graph Generator (NGG) revolutionizes graph generation by efficiently capturing specific properties through conditioned latent diffusion models.
Resumé
Introduction
Graph generation is crucial in machine learning.
Existing methods struggle with high-dimensional complexity.
Graph Generative Models
Five families of models: Auto-Regressive, Variational Autoencoders, GANs, Normalizing Flows, Diffusion models.
Models focus on specific graph types.
Neural Graph Generator
Utilizes latent diffusion models for graph generation.
Employs variational graph autoencoder for compression.
Experimental Evaluation
Trained on a dataset of synthetic graphs.
Outperforms baseline VGAE model.
Examples of Generated Graphs
Two graphs generated with specific properties.
Model can generate diverse graphs.
Uniqueness of Generated Graphs
All generated graphs are unique.
Conclusion
NGG offers efficient and accurate graph generation with specific properties.
Statistik
NGG revolutioniert die Graphengenerierung durch konditionierte latente Diffusionsmodelle.
Citater
NGG bietet eine effiziente Lösung für die Generierung von Graphen mit spezifischen Eigenschaften.