  Graph Neural Networks (GNNs) are gaining extensive attention for their
application in graph data. However, the black-box nature of GNNs prevents users
from understanding and trusting the models, thus hampering their applicability.
Whereas explaining GNNs remains a challenge, most existing methods fall into
approximation based and perturbation based approaches with suffer from
faithfulness problems and unnatural artifacts, respectively. To tackle these
problems, we propose DEGREE \degree to provide a faithful explanation for GNN
predictions. By decomposing the information generation and aggregation
mechanism of GNNs, DEGREE allows tracking the contributions of specific
components of the input graph to the final prediction. Based on this, we
further design a subgraph level interpretation algorithm to reveal complex
interactions between graph nodes that are overlooked by previous methods. The
efficiency of our algorithm can be further improved by utilizing GNN
characteristics. Finally, we conduct quantitative and qualitative experiments
on synthetic and real-world datasets to demonstrate the effectiveness of DEGREE
on node classification and graph classification tasks.
