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.
翻译:不同大脑区域之间复杂的结构性和功能性联系是多式脑网络的特点,为精神疾病分析提供了新的手段。最近,图表神经网络(GNN)已成为分析图表结构数据的实际模式。然而,如何利用GNNS从大脑网络中以多种方式获取有效表现仍然很少探索。此外,由于大脑网络没有提供初始节点特征,如何设计信息化节点属性,如何为GNS提供学习的优势重量尚未解决。为此,我们为多式脑网络开发了新型的多视图GNN。特别是,我们把每种模式视为脑网络的视角,并采用反向学习的多式聚合方法。然后,我们提出GNNN模式,利用信息传递计划,根据程度统计和大脑区域连接传播信息。关于两个真实世界疾病数据集(艾滋病毒和比波拉)的广泛实验,展示了我们所提议的方法相对于最新基线的有效性。