Explaining Dynamic Graph Neural Networks via Relevance Back-propagation

Understand

Graph Neural Networks (GNNs) have shown remarkable effectiveness in capturing abundant information in graph-structured data.

• However, the black-box nature of GNNs hinders users from understanding and trusting the models, thus leading to difficulties in their applications.
• While recent years witness the prosperity of the studies on explaining GNNs, most of them focus on static graphs, leaving the explanation of dynamic GNNs nearly unexplored.
• It is challenging to explain dynamic GNNs, due to their unique characteristic of time-varying graph structures.