Explaining-the-Explainers-in-Graph-Neural-Networks

Repository associated to the paper: "Explaining the Explainers in Graph Neural Networks: a Comparative Study"

Notebooks

1. [GC] Load trained model: it is a notebook to load and explore the trained model
2. [GC] Evaluating explanations: shows how to compute filter, plausibility and fidelity

Datasets

In the folder dataset, you can find the networks used in this study!
Into the folder Dataset you can find a readme explaining how to generate and explore the datasets

Explanations

It contains the explanations produced by different explainers on different GNNs and datasets. Have a look at the README inside the folder for more info

Models

It contains all the trained model used in our work! It also contains the classes to load the trained model. The notebook [GC] Load trained model show how to load and evaluate a trained model in graph classification.

Experiments and code

Work in progress!

Citation

@article{longa2022explaining,
  title={Explaining the Explainers in Graph Neural Networks: a Comparative Study},
  author={Longa, Antonio and Azzolin, Steve and Santin, Gabriele and Cencetti, Giulia and Li{\`o}, Pietro and Lepri, Bruno and Passerini, Andrea},
  journal={arXiv preprint arXiv:2210.15304},
  year={2022}
}