GIND: Optimization-Induced Graph Implicit Nonlinear Diffusion

This repo contains the implementation of the Graph Implicit Nonlinear Diffusion model, as described in our paper:

Qi Chen, Yifei Wang, Yisen Wang, Jiansheng Yang, Zhouchen Lin: [Optimization-Induced Graph Implicit Nonlinear Diffusion] (ICML 2022)

GIND is implemented in PyTorch and utilizes the PyTorch Geometric (PyG) library.

Requirements

• Install PyTorch >= 1.7.0
• Install PyTorch Geometric >= 1.7.0

We use Hydra to manage hyperparameter configurations.

Project Structure

• model/ contains the model architecture of GIND
• libs/ contains helpful functions used in our experiments
• node_classification/ includes experiments to evaluate GIND on node classification tasks
• graph_classification/ includes experiments to evaluate GIND on graph classification tasks

Cite

If you find this repo useful, please cite:

@inproceedings{chen2022optimization,
  title={Optimization-Induced Graph Implicit Nonlinear Diffusion},
  author={Chen, Qi and Wang, Yifei and Wang, Yisen and Yang, Jiansheng and Lin, Zhouchen},
  booktitle={International Conference on Machine Learning (ICML)},
  year={2022},
}