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.

提出了一个可解释的 GNN 框架 BrainNNExplainer，用于脑网络分析，主要由两个联合学习的模块组成：为脑网络设计的骨干预测模型和突出疾病特定脑网络连接的解释生成器，广泛实验证明了 BrainNNExplainer 的独特解释性和优秀性能。