• 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 this new version, AutoGL supports neural architecture search (NAS) to customize architectures for the given datasets and tasks. AutoGL also supports sampling now to perform tasks on large datasets, including node-wise sampling, layer-wise sampling, and sub-graph sampling. The link prediction task is now also supported! Learn more in our tutorial.
• 2021.04.16 Our survey paper about automated machine learning on graphs is accepted by IJCAI! See more here.
• 2021.04.10 Our paper AutoGL: A Library for Automated Graph Learning is accepted by 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 conduct autoML on graph datasets and tasks easily and quickly. See our documentation for detailed information!

The workflow below shows the overall framework of AutoGL.

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

Different graph-based machine learning tasks are handled 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 Graphlets EigenGNN 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

Please cite our paper as follows if you find our code useful:

@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}
}

You may also find our survey paper helpful:

@article{zhang2021automated,
  title={Automated Machine Learning on Graphs: A Survey},
  author={Zhang, Ziwei and Wang, Xin and Zhu, Wenwu},
  booktitle = {Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, {IJCAI-21}},
  year={2021},
  note={Survey track}
}

License

Notice that we follow Apache license across the entire codebase from v0.2.