1. Amara, K., Ying, R., Zhang, Z., Han, Z., Shan, Y., Brandes, U., Schemm, S., Zhang, C., 2022. Graphframex: Towards systematic evaluation of explainability methods for graph neural networks. arXiv preprint arXiv:2206.09677 .
2. Baldassarre F. Azizpour H. 2019. Explainability techniques for graph convolutional networks. arXiv preprint arXiv:1905.13686 .
3. Battaglia P.W. Hamrick J.B. Bapst V. Sanchez-Gonzalez A. Zambaldi V. Malinowski M. Tacchetti A. Raposo D. Santoro A. Faulkner R. et al. 2018. Relational inductive biases deep learning and graph networks. arXiv preprint arXiv:1806.01261 .
4. Chereda, H., Bleckmann, A., Kramer, F., Leha, A., Beissbarth, T., 2019. Utilizing molecular network information via graph convolutional neural networks to predict metastatic event in breast cancer., in: GMDS, pp. 181--186.
5. GraphSVX: Shapley Value Explanations for Graph Neural Networks