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Towards Training GNNs using Explanation Directed Message Passing

This repository contains source code necessary to reproduce some of the main results in the paper:

If you use this software, please consider citing:

  @inproceedings{giunchiglia2022towards,
    title={Towards Training {GNN}s using Explanation Directed Message Passing},
    author={Valentina Giunchiglia and Chirag Varun Shukla and Guadalupe Gonzalez and Chirag Agarwal},
    booktitle={The First Learning on Graphs Conference},
    year={2022},
    url={https://openreview.net/forum?id=_nlbNbawXDi}
    }

a) EXPASS investigates the problem of injecting explanations into the message-passing framework to increase the expressive power and performance of GNNs. b) Shown is the general message passing scheme where, for node u, messages are aggregated from nodes vi ∈ Nu in the 1-hop neighborhood of u. c) EXPASS injects explanations into the message passing framework translates by masking out messages from neighboring nodes vi ∈ Nu with explanation scores suvi ≈ 0 when u is correctly classified

1. Setup

Installing software

The packages used and their versions are available in the environmemt.yml file. This repository is built using PyTorch. You can install the necessary libraries by following these commnads:

  1. Create a conda environment with python=3.9 conda create -n logml2 python=3.9

  2. Install torch_geometric from the python wheels following the instructions here

    a. Find the torch version python -c "import torch; print(torch.__version__)" and save the output as bash variable TORCH

    b. Find the cuda version python -c "import torch; print(torch.version.cuda)" and save the output as bash variable CUDA

    c. Run python -m pip install torch-scatter -f https://data.pyg.org/whl/torch-${TORCH}+${CUDA}.html. The python -m pip is important. It may take a while to install.

    d. Run python -m pip install torch-sparse -f https://data.pyg.org/whl/torch-${TORCH}+${CUDA}.html

  3. Run python -m pip install torch-geometric==2.0.4

  4. Install missing pypi dependencies with python -m pip install pgmpy matplotlib ipdb pandas

  5. Install graphxai following the instructions here.

2. Datasets

We run thee experiments on 4 real-world datasets (DD, Proteins, Mutag and Alkane). To choose the dataset, you have to change the --dataset argument.

3. Usage

To run all combinations of architectures and datasets, both for vanilla and EXPASS, you can run the bash script run.sh as follows:

  bash run.sh

To run the vanilla version of the scripts with the deafult parametrs, you can use:

   python train.py \
      --dataset <DATASET> \
      --arch <ARCHITECTURE> \
      --vanilla_mode

To run the EXPASS version of the scripts with the default parameters, you can use:

python train.py \
   --dataset <DATASET>  \
   --arch <ARCHITECTURE> \
   --explainer <EXPLAINER>

4. License

Note that the code in this repository is licensed under MIT License. Please carefully check them before use.

5. Questions?

If you have questions/suggestions, please feel free to email one of the authors (Valentina Giunchiglia, or Chirag Shukla) or create github issues.