Explainability in Graph Neural Networks: A Taxonomic Survey

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. 45; no. 5; pp. 1 - 19
• Main Authors: Yuan, Hao, Yu, Haiyang, Gui, Shurui, Ji, Shuiwang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial Intelligence; Benchmarking; Biological system modeling; Data models; evaluation; explainability; Graph analysis; Graph neural networks; interpretability; Machine learning; Neural networks; Neural Networks, Computer; Predictive models; survey; Systematics; Task analysis; Taxonomy; Test stands

Deep learning methods are achieving ever-increasing performance on many artificial intelligence tasks. A major limitation of deep models is that they are not amenable to interpretability. This limitation can be circumvented by developing post hoc techniques to explain predictions, giving rise to the...