gnn-explainer

This repository contains the source code for the paper GNNExplainer: Generating Explanations for Graph Neural Networks by Rex Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik & Jure Leskovec, presented at NeurIPS 2019.

[Arxiv] [BibTex] [Google Scholar]

@misc{ying2019gnnexplainer,
    title={GNNExplainer: Generating Explanations for Graph Neural Networks},
    author={Rex Ying and Dylan Bourgeois and Jiaxuan You and Marinka Zitnik and Jure Leskovec},
    year={2019},
    eprint={1903.03894},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

Using the explainer

Installation

See INSTALLATION.md

Replicating the paper's results

Training a GCN model

This is the model that will be explained. We do provide pre-trained models for all of the experiments that are shown in the paper. To re-train these models, run the following:

python train.py --dataset=EXPERIMENT_NAME

where EXPERIMENT_NAME is the experiment you want to replicate.

For a complete list of options in training the GCN models:

python train.py --help

TODO: Explain outputs

Explaining a GCN model

To run the explainer, run the following:

python explainer_main.py --dataset=EXPERIMENT_NAME

where EXPERIMENT_NAME is the experiment you want to replicate.

For a complete list of options provided by the explainer:

python train.py --help

Visualizing the explanations

Tensorboard

The result of the optimization can be visualized through Tensorboard.

tensorboard --logdir log

You should then have access to visualizations served from localhost.

Jupyter Notebook

We provide an example visualization through Jupyter Notebooks in the notebook folder. To try it:

jupyter notebook

The default visualizations are provided in notebook/GNN-Explainer-Viz.ipynb.

Note: For an interactive version, you must enable ipywidgets
jupyter nbextension enable --py widgetsnbextension

You can now play around with the mask threshold in the GNN-Explainer-Viz-interactive.ipynb.

TODO: Explain outputs + visualizations + baselines

D3,js

We provide export functionality so the generated masks can be visualized in other data visualization frameworks, for example d3.js. We provide an example visualization in Observable.

Included experiments

Name	`EXPERIMENT_NAME`	Description
Synthetic #1	`syn1`	Random BA graph with House attachments.
Synthetic #2	`syn2`	Random BA graph with community features.
Synthetic #3	`syn3`	Random BA graph with grid attachments.
Synthetic #4	`syn4`	Random Tree with cycle attachments.
Synthetic #5	`syn5`	Random Tree with grid attachments.
Enron	`enron`	Enron email dataset source.
PPI	`ppi_essential`	Protein-Protein interaction dataset.

Reddit*	`REDDIT-BINARY`	Reddit-Binary Graphs (source).
Mutagenicity*	`Mutagenicity`	Predicting the mutagenicity of molecules (source).
Tox 21*	`Tox21_AHR`	Predicting a compound's toxicity (source).

Datasets with a * are passed with the --bmname parameter rather than --dataset as they require being downloaded manually.

TODO: Provide all data for experiments packaged so we don't have to split the two.

Using the explainer on other models

A graph attention model is provided. This repo is still being actively developed to support other GNN models in the future.

Changelog

See CHANGELOG.md

philo-vanguard/gnn-model-explainer-1