Auto Graph Learning
An autoML framework and toolkit for automating machine learning tasks on graph-structured data.
What is Auto Graph Learning?
AutoGL is an automated machine learning (AutoML) framework and toolkit designed specifically for graph-structured data. It automates the entire machine learning pipeline for graph tasks, including feature engineering, neural architecture search, hyperparameter optimization, and model ensemble, reducing the need for manual intervention and expertise in graph learning.
Researchers and developers working on graph-based machine learning who want to automate model development and experimentation, particularly those in academic or industrial settings focusing on node classification, link prediction, or graph classification.
AutoGL provides a comprehensive, graph-specific AutoML solution that integrates with popular graph libraries like PyG and DGL, offering a unified framework to automate and accelerate graph learning workflows while maintaining flexibility for custom implementations.
Overview
An autoML framework & toolkit for machine learning on graphs.
Use Cases
Best For
- Automating graph neural network architecture search for specific datasets
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