GraphLIME: Local Interpretable Model Explanations for Graph Neural Networks

Understand

Graph structured data has wide applicability in various domains such as physics, chemistry, biology, computer vision, and social networks, to name a few.

• Recently, graph neural networks (GNN) were shown to be successful in effectively representing graph structured data because of their good performance and generalization ability.
• GNN is a deep learning based method that learns a node representation by combining specific nodes and the structural/topological information of a graph.
• However, like other deep models, explaining the effectiveness of GNN models is a challenging task because of the complex nonlinear transformations made over the iterations.