Global Concept-Based Interpretability for Graph Neural Networks via Neuron Analysis


  • Han Xuanyuan University of Cambridge
  • Pietro Barbiero University of Cambridge
  • Dobrik Georgiev University of Cambridge
  • Lucie Charlotte Magister University of Cambridge
  • Pietro Liò University of Cambridge



ML: Transparent, Interpretable, Explainable ML, ML: Graph-based Machine Learning


Graph neural networks (GNNs) are highly effective on a variety of graph-related tasks; however, they lack interpretability and transparency. Current explainability approaches are typically local and treat GNNs as black-boxes. They do not look inside the model, inhibiting human trust in the model and explanations. Motivated by the ability of neurons to detect high-level semantic concepts in vision models, we perform a novel analysis on the behaviour of individual GNN neurons to answer questions about GNN interpretability. We propose a novel approach for producing global explanations for GNNs using neuron-level concepts to enable practitioners to have a high-level view of the model. Specifically, (i) to the best of our knowledge, this is the first work which shows that GNN neurons act as concept detectors and have strong alignment with concepts formulated as logical compositions of node degree and neighbourhood properties; (ii) we quantitatively assess the importance of detected concepts, and identify a trade-off between training duration and neuron-level interpretability; (iii) we demonstrate that our global explainability approach has advantages over the current state-of-the-art -- we can disentangle the explanation into individual interpretable concepts backed by logical descriptions, which reduces potential for bias and improves user-friendliness.




How to Cite

Xuanyuan, H., Barbiero, P., Georgiev, D., Magister, L. C., & Liò, P. (2023). Global Concept-Based Interpretability for Graph Neural Networks via Neuron Analysis. Proceedings of the AAAI Conference on Artificial Intelligence, 37(9), 10675-10683.



AAAI Technical Track on Machine Learning IV