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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
  1. 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.

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