GNN Explainability Framework

Node Classification Tasks

Explainer	Paper
Distance	Shortest Path Distance Approximation using Deep learning Techniques
PageRank	The PageRank Citation Ranking: Bringing Order to the Web
SA	Explainability Techniques for Graph Convolutional Networks.
Grad-CAM	Explainability Methods for Graph Convolutional Neural Networks.
Integrated Gradients	Axiomatic Attribution for Deep Networks
GNNExplainer	GNNExplainer: Generating Explanations for Graph Neural Networks
PGExplainer	Parameterized Explainer for Graph Neural Network
SubgraphX	On Explainability of Graph Neural Networks via Subgraph Exploration
PGM-Explainer	PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks

Installation

Requirements

• CPU or NVIDIA GPU, Linux, Python 3.7
• PyTorch >= 1.5.0, other packages

1. Pytorch Geometric. Official Download.

# We use TORCH version 1.6.0
CUDA=cu111
TORCH=1.9.1
pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-${TORCH}+${CUDA}.html
pip install torch-sparse -f https://pytorch-geometric.com/whl/torch-${TORCH}+${CUDA}.html
pip install torch-cluster -f https://pytorch-geometric.com/whl/torch-${TORCH}+${CUDA}.html
pip install torch-spline-conv -f https://pytorch-geometric.com/whl/torch-${TORCH}+${CUDA}.html
pip install torch-geometric==2.0.3

2. Other packages

pip install tqdm matplotlib argparse json jupyterlab notebook pgmpy captum

Datasets

1. The processed raw data for datasets ba_house, ba_community, ba_grid, tree_cycle, tree_grid, ba_bottle is available in the data/syn folder.
2. The processed raw data for datasets cora, citeseer, pubmed, cornell, texas, wisconsin, chameleon, squirrel, actor will be automatically downloaded when training models.

Python code map

.
├── dataset
│   ├── __init__.py
│   ├── data_utils.py
│   ├── gen_mutag.py
│   ├── gen_real.py
│   ├── gen_syn.py
│   ├── mutag_utils.py
│   └── syn_utils
│       ├── featgen.py
│       ├── gengraph.py
│       ├── gengroundtruth.py
│       └── synthetic_structsim.py
├── evaluate
│   ├── __init__.py
│   ├── accuracy.py
│   ├── fidelity.py
│   └── mask_utils.py
├── explainer
│   ├── __init__.py
│   ├── genmask.py
│   ├── gnnexplainer.py
│   ├── graph_explainer.py
│   ├── node_explainer.py
│   ├── pgmexplainer.py
│   ├── pgexplainer.py
│   ├── shapley.py
│   └── subgraphx.py
├── gnn
│   ├── __init__.py
│   ├── eval.py
│   ├── model.py
│   └── train.py
├── main.py
└── utils
    ├── __init__.py
    ├── gen_utils.py
    ├── graph_utils.py
    ├── io_utils.py
    ├── math_utils.py
    ├── parser_utils.py
    └── plot_utils.py

Node Classification

python3 code/main.py --dataset [dataset-name] --explain_graph False --explainer_name [explainer_name]

• dataset-name:
  • synthetic: ba_house, ba_grid, tree_cycle, tree_grid, ba_bottle
  • real-world: cora, pubmed, citeseer, facebook, chameleon, squirrel, texas, wisconsin, cornell, actor
• explainer_name: random, pagerank, distance, sa, ig, gradcam, occlusion, basic_gnnexplainer, gnnexplainer, subgraphx, pgmexplainer, pgexplainer

Note that gradcam is only available for synthetic datasets.

Mask transformation

To compare the methods, we adopt separately three strategies to cut off the masks:

1. Sparsity

2. Threshold

3. Topk

This can be changed by changing the --strategy parameter. Choices are [topk, sparsity,threshold]. The default strategy is topk. You adjust the level of transformation with the --params_list parameter. Here, you define the list of transformation values. Default list is "5,10"

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

Tuning the mask sparsity/threshold/top-k values.

Included experiments

Name	EXPERIMENT_NAME	Description
Barabasi-House	ba_house	Random BA graph with House attachments.
Barabasi-Grid	ba_grid	Random BA graph with grid attachments.
Tree-Cycle	tree_cycle	Random Tree with cycle attachments.
Tree-Grid	tree_grid	Random Tree with grid attachments.
Barabasi-Bottle	ba_bottle	Random BA graph with bottle attachments.
MUTAG	mutag	Mutagenecity Predicting the mutagenicity of molecules (source).
Cora	cora	Citation network (source).
Pubmed	pubmed	PubMed network (source).
Citeseer	citeseer	Citeseer network (source).
FacebookPagePage	facebook	Facebook Page-Page network dataset
Chameleon	chameleon	Wikipedia dataset
Squirrel	squirrel	Wikipedia dataset
Texas	texas	WebKB dataset
Wisconsin	wisconsin	WebKB dataset
Cornell	cornell	WebKB dataset
Actor	actor	Film-director-actor-writer network (Actor)

Using the explainer on other models

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

Citation

Please cite our paper if you find the repository useful.