Awesome

IDGL

Code & data accompanying the NeurIPS 2020 paper "Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings".

Architecture

Get started

Prerequisites

This code is written in python 3. You will need to install a few python packages in order to run the code. We recommend you to use virtualenv to manage your python packages and environments. Please take the following steps to create a python virtual environment.

• If you have not installed virtualenv, install it with pip install virtualenv.
• Create a virtual environment with virtualenv venv.
• Activate the virtual environment with source venv/bin/activate.
• Install the package requirements with pip install -r requirements.txt.

Run the IDGL & IDGL-Anch models

• Cd into the src folder

• Run the IDGL model and report the performance

       python main.py -config config/cora/idgl.yml
  

• Run the IDGL-Anch model and report the performance

       python main.py -config config/cora/idgl_anchor.yml
  

• Notes:

  • You can find the output data in the out_dir folder specified in the config file.
  • You can add --multi_run in the command to run multiple times with different random seeds. Please see config/cora/idgl.yml for example.
  • To run IDGL & IDGL-Anch without the iterative learning or graph regularization components, please set max_iter to 0 or graph_learn_regularization to False in the config file.
  • You can download the 20News data from here, and move it to the data folder.

Reference

If you found this code useful, please consider citing the following paper:

Yu Chen, Lingfei Wu and Mohammed J. Zaki. "Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings." In Proceedings of the 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Dec 6-12, 2020.

@article{chen2020iterative,
  title={Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings},
  author={Chen, Yu and Wu, Lingfei and Zaki, Mohammed},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  year={2020}
}