GraphLIME: Local Interpretable Model Explanations for Graph Neural Networks

• Published in: IEEE Transactions on Knowledge and Data Engineering Vol. 35; no. 7; pp. 6968 - 6972
• Main Authors: Huang, Qiang, Yamada, Makoto, Tian, Yuan, Singh, Dinesh, Chang, Yi
• Format: Journal Article
• Language: English; Japanese
• Published: New York IEEE 01.07.2023; Institute of Electrical and Electronics Engineers (IEEE); The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Computational modeling; Data models; explanation; Feature extraction; Graph neural networks; Graph theory; Graphical representations; interpretability; Kernel; Mathematical models; Predictive models; Structured data; Toy manufacturing industry

Recently, graph neural networks (GNN) were shown to be successful in effectively representing graph structured data because of their good performance and generalization ability. However, explaining the effectiveness of GNN models is a challenging task because of the complex nonlinear transformations...