The code in this repository is now integrated into KGCNN repository.
This repository provides a way to explain Keras/Tensorflow Graph Neural Network decisions. The basic idea behind the generation method for explanations is heavily inspired by the paper "GNNExplainer: Generating Explanations for Graph Neural Networks" by Ying et al., although it differs in some aspects.
.
├── LICENSE # License file
├── README.md # This README
├── abschlussbericht.ipynb # Final report on the project
├── examples
│ ├── cora
│ │ ├── data # Cora dataset
│ │ ├── dataloader.py # Data loading utility
│ │ └── example_notebook.ipynb # Example for CORA
│ └── mutagenicity
│ ├── data # Mutagenicity dataset
│ ├── dataloader.py # Data loading utility
│ ├── example_notebook.ipynb # Mask on input
│ ├── example_notebook2.ipynb # Mask on GNN operation
│ └── example_notebook3.ipynb # Multi-Instance explanations
├── gnnx # gnnx-Package
│ ├── __init__.py
│ └── gnnx.py # Core implemenation
├── pyproject.toml
└── setup.py
pip install -e .
# Configuration
compile_options = {'loss': 'binary_crossentropy'}
fit_options = {'epochs': 100}
config = {
'edge_mask_loss_weight': 0.01, # λ_M
'edge_mask_norm_ord': 1, # p_M
'feature_mask_loss_weight': 0.01, # λ_F
'feature_mask_norm_ord': 1, # p_F
'node_mask_loss_weight': 0, # λ_N
'node_mask_norm_ord': 1} # p_N
# Setup. Instantiating the GNNExplainer
explainer = gnnx.GNNExplainer(
gnn, # Implements gnnx.GNNInterface
compile_options=compile_options,
fit_options=fit_options,
gnnexplaineroptimizer_options=config)
# Explaining Decisions
explainer.explain(input_instance)
explanation = explainer.get_explanation()
explainer.present_explanation(explanation) For more examples look inside the examples directory.
