Valid, Sparse, and Stable Explanations in Graph Neural Networks

See requirements.txt for the main python packages used to run this repository with python 3.7.

The real-world datasets will be downloaded via pytorch-geometric. For the synthetic dataset, we included the file generate_gnnexplainer_dataset.py and in data/syn2.npz our resulting graph.

You can simply run

python3 execution.py

to get explanations for the default setting: 10 nodes for Cora and GCN with tau=0.85.

We included the save points of the model and the randomly selected nodes in the results directory.

Running evaluate_zorro.py will create csv files with the evaluate explanations.