  Interpretable brain network models for disease prediction are of great value
for the advancement of neuroscience. GNNs are promising to model complicated
network data, but they are prone to overfitting and suffer from poor
interpretability, which prevents their usage in decision-critical scenarios
like healthcare. To bridge this gap, we propose BrainNNExplainer, an
interpretable GNN framework for brain network analysis. It is mainly composed
of two jointly learned modules: a backbone prediction model that is
specifically designed for brain networks and an explanation generator that
highlights disease-specific prominent brain network connections. Extensive
experimental results with visualizations on two challenging disease prediction
datasets demonstrate the unique interpretability and outstanding performance of
BrainNNExplainer.
