https://github.com/alexduvalinho/gnne_eval
Evaluate GraphSHAP using GNNE's eval pipeline
https://github.com/alexduvalinho/gnne_eval
Last synced: 3 months ago
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Evaluate GraphSHAP using GNNE's eval pipeline
- Host: GitHub
- URL: https://github.com/alexduvalinho/gnne_eval
- Owner: AlexDuvalinho
- License: apache-2.0
- Created: 2020-10-01T08:29:33.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2021-04-14T14:22:59.000Z (about 4 years ago)
- Last Synced: 2025-01-05T21:42:33.933Z (5 months ago)
- Language: Python
- Size: 598 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
Awesome Lists containing this project
README
# gnn-explainer
This repository contains the source code for the paper `GNNExplainer: Generating Explanations for Graph Neural Networks` by [Rex Ying](https://cs.stanford.edu/people/rexy/), [Dylan Bourgeois](https://dtsbourg.me/), [Jiaxuan You](https://cs.stanford.edu/~jiaxuan/), [Marinka Zitnik](http://helikoid.si/cms/) & [Jure Leskovec](https://cs.stanford.edu/people/jure/), presented at [NeurIPS 2019](nips.cc).
[[Arxiv]](https://arxiv.org/abs/1903.03894) [[BibTex]](https://dblp.uni-trier.de/rec/bibtex/journals/corr/abs-1903-03894) [[Google Scholar]](https://scholar.google.com/scholar?q=GNNExplainer%3A%20Generating%20Explanations%20for%20Graph%20Neural%20Networks%20Rex%20arXiv%202019)
```
@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](#TODO) 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](http://observablehq.com). We provide [an example visualization in Observable](https://observablehq.com/d/00c5dc74f359e7a1).
#### 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](https://www.cs.cmu.edu/~enron/). |
| PPI | `ppi_essential` | Protein-Protein interaction dataset. |
| | | |
| Reddit* | `REDDIT-BINARY` | Reddit-Binary Graphs ([source](https://ls11-www.cs.tu-dortmund.de/staff/morris/graphkerneldatasets)). |
| Mutagenicity* | `Mutagenicity` | Predicting the mutagenicity of molecules ([source](https://ls11-www.cs.tu-dortmund.de/staff/morris/graphkerneldatasets)). |
| Tox 21* | `Tox21_AHR` | Predicting a compound's toxicity ([source](https://ls11-www.cs.tu-dortmund.de/staff/morris/graphkerneldatasets)). |
> 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](#)