Multimodal brain networks characterize complex connectivities among different
brain regions from both structural and functional aspects and provide a new
means for mental disease analysis. Recently, Graph Neural Networks (GNNs) have
become a de facto model for analyzing graph-structured data. However, how to
employ GNNs to extract effective representations from brain networks in
multiple modalities remains rarely explored. Moreover, as brain networks
provide no initial node features, how to design informative node attributes and
leverage edge weights for GNNs to learn is left unsolved. To this end, we
develop a novel multiview GNN for multimodal brain networks. In particular, we
regard each modality as a view for brain networks and employ contrastive
learning for multimodal fusion. Then, we propose a GNN model which takes
advantage of the message passing scheme by propagating messages based on degree
statistics and brain region connectivities. Extensive experiments on two
real-world disease datasets (HIV and Bipolar) demonstrate the effectiveness of
our proposed method over state-of-the-art baselines.

我们提出了一种基于多视图 GNN 和对比学习的方法，用于对多模态脑网络进行分析，并通过在两个现实疾病数据集上的实验证明了该方法的有效性。