This project is a PyTorch implementation of Model-Agnostic Augmentation for Accurate Graph Classification (WWW 2022). This paper proposes NodeSam and SubMix, two novel algorithms for model-agnostic graph augmentation.

Our implementation is based on Python 3.7 and PyTorch Geometric. Please see the full list of packages required to run our codes in requirements.txt.

• Python 3.7
• PyTorch 1.4.0
• PyTorch Geometric 1.6.3

PyTorch Geometric requires a separate installation process from the other packages. We included install.sh to guide the installation process of PyTorch Geometric based on the OS and CUDA version. The code includes the cases for Linux + CUDA 10.0, Linux + CUDA 10.1, and MacOS + CPU.

We use 9 datasets in our work, which are not included in this repository due to their size but can be downloaded easily by PyTorch Geometric. You can run data.py in the src directory to download the datasets in the data/graphs directory. Our split indices in data/splits are also based on these datasets.

We included demo.sh, which reproduces the experimental results of our paper. The code automatically downloads the datasets and trains a GIN classifier with all of our proposed approaches for graph augmentation. In other words, you just have to type the following command.

bash demo.sh

This demo script uses all of your GPUs by default and runs four workers for each GPU to reduce the running time. You can change experimental arguments such as the number of workers in run.py and the other hyperparameters such as the number of epochs, batch size, or the initial learning rate in main.py. Since run.py is a wrapper script for the parallel execution of main.py, all optional arguments given to run.py are passed also to main.py.

Please cite the following paper if you use our code:

@inproceedings{DBLP:conf/www/YooSK22,
  author    = {Jaemin Yoo and
               Sooyeon Shim and
               U Kang},
  editor    = {Fr{\'{e}}d{\'{e}}rique Laforest and
               Rapha{\"{e}}l Troncy and
               Elena Simperl and
               Deepak Agarwal and
               Aristides Gionis and
               Ivan Herman and
               Lionel M{\'{e}}dini},
  title     = {Model-Agnostic Augmentation for Accurate Graph Classification},
  booktitle = {{WWW} '22: The {ACM} Web Conference 2022, Virtual Event, Lyon, France,
               April 25 - 29, 2022},
  pages     = {1281--1291},
  publisher = {{ACM}},
  year      = {2022},
  url       = {https://doi.org/10.1145/3485447.3512175},
  doi       = {10.1145/3485447.3512175},
  timestamp = {Thu, 23 Jun 2022 19:54:34 +0200},
  biburl    = {https://dblp.org/rec/conf/www/YooSK22.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}