• Auto Graph Learning
• • News!
  • Introduction
  • Installation
  • • Requirements
    • Installation
    • • Install from pip
      • Install from source
      • Install for development
  • Documentation
  • Cite
  • License

Auto Graph Learning

An autoML framework & toolkit for machine learning on graphs.

Actively under development by @THUMNLab

Feel free to open issues or contact us at autogl@tsinghua.edu.cn if you have any comments or suggestions!

News!

• 2021.07.11 New version! v0.2.0-pre is here! In new version, AutoGL support neural architecture search to customize the architectures for the given datasets and tasks. AutoGL also support sampling now to perform tasks on large datasets, including node-wise sampling, layer-wise sampling and sub-graph sampling. Link prediction task is now also supported! Learn more in our tutorial.
• 2021.04.10 Our paper AutoGL: A Library for Automated Graph Learning are accepted in ICLR 2021 Workshop on Geometrical and Topological Representation Learning! You can cite our paper following methods here.

Introduction

AutoGL is developed for researchers and developers to quickly conduct autoML on the graph datasets & tasks. See our documentation for detailed information!

The workflow below shows the overall framework of AutoGL.

AutoGL uses datasets to maintain dataset for graph-based machine learning, which is based on Dataset in PyTorch Geometric with some support added to corporate with the auto solver framework.

Different graph-based machine learning tasks are solved by different AutoGL solvers, which make use of five main modules to automatically solve given tasks, namely auto feature engineer, neural architecture search, auto model, hyperparameter optimization, and auto ensemble.

Currently, the following algorithms are supported in AutoGL:

Feature Engineer	Model	NAS	HPO	Ensemble
Generators graphlet eigen more ... Selectors SeFilterConstant gbdt Graph netlsd NxAverageClustering more ...	Node Classification GCN GAT GraphSAGE Graph Classification GIN TopKPool	Algorithms Random RL more ... Spaces SinglePath GraphNas more ... Estimators Oneshot Scratch	Grid Random Anneal Bayes CAMES MOCAMES Quasi random TPE AutoNE	Voting Stacking

This toolkit also serves as a framework for users to implement and test their own autoML or graph-based machine learning models.

Installation

Requirements

Please make sure you meet the following requirements before installing AutoGL.

1. Python >= 3.6.0

2. PyTorch (>=1.6.0)

   see https://pytorch.org/ for installation.

3. PyTorch Geometric (>=1.7.0)

   see https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html for installation.

Installation

Install from pip

Run the following command to install this package through pip.

pip install autogl

Install from source

Run the following command to install this package from the source.

git clone https://github.com/THUMNLab/AutoGL.git
cd AutoGL
python setup.py install

Install for development

If you are a developer of the AutoGL project, please use the following command to create a soft link, then you can modify the local package without install them again.

pip install -e .

Documentation

Please refer to our documentation to see the detailed documentation.

You can also make the documentation locally. First, please install sphinx and sphinx-rtd-theme:

pip install -U Sphinx
pip install sphinx-rtd-theme

Then, make an html documentation by:

cd docs
make clean && make html

The documentation will be automatically generated under docs/_build/html

Cite

You can cite our paper as follows if you use this code in your own work:

@inproceedings{
guan2021autogl,
title={Auto{GL}: A Library for Automated Graph Learning},
author={Chaoyu Guan and Ziwei Zhang and Haoyang Li and Heng Chang and Zeyang Zhang and Yijian Qin and Jiyan Jiang and Xin Wang and Wenwu Zhu},
booktitle={ICLR 2021 Workshop on Geometrical and Topological Representation Learning},
year={2021},
url={https://openreview.net/forum?id=0yHwpLeInDn}
}

License

We follow MIT license across the entire codebase.