Graph neural networks (GNNs) have recently grown in popularity for disease prediction. Existing GNN-based methods primarily build the graph topological structure around a single modality and combine it with other modalities to acquire feature representations of acquisitions. The complicated relationship in each modality, however, may not be well highlighted due to its specificity. Further, relatively shallow networks restrict adequate extraction of high-level features, affecting disease prediction performance. Accordingly, this paper develops a new interactive deep cascade spectral graph convolutional network with multi-relational graphs (IDCGN) for disease prediction tasks. Its crucial points lie in constructing multiple relational graphs and dual cascade spectral graph convolution branches with interaction (DCSGBI). Specifically, the former designs a pairwise imaging-based edge generator and a pairwise non-imaging-based edge generator from different modalities by devising two learnable networks, which adaptively capture graph structures and provide various views of the same acquisition to aid in disease diagnosis. Again, DCSGBI is established to enrich high-level semantic information and low-level details of disease data. It devises a cascade spectral graph convolution operator for each branch and incorporates the interaction strategy between different branches into the network, successfully forming a deep model and capturing complementary information from diverse branches. In this manner, more favorable and sufficient features are learned for a reliable diagnosis. Experiments on several disease datasets reveal that IDCGN exceeds state-of-the-art models and achieves promising results.

中文翻译：

---

一种用于疾病预测的具有多关系图的新型交互式深度级联谱图卷积网络

图神经网络（GNN）最近在疾病预测方面越来越受欢迎。现有的基于 GNN 的方法主要围绕单一模态构建图拓扑结构，并将其与其他模态结合起来以获得采集的特征表示。然而，由于其特殊性，每种模式中的复杂关系可能无法得到很好的强调。此外，相对较浅的网络限制了高级特征的充分提取，影响了疾病预测性能。因此，本文开发了一种新的具有多关系图的交互式深度级联谱图卷积网络（IDCGN），用于疾病预测任务。其关键点在于构建多个关系图和具有交互作用的双级联谱图卷积分支（DCSGBI）。具体来说，前者通过设计两个可学习网络，从不同模态设计了基于成对成像的边缘生成器和成对非基于成像的边缘生成器，这两个网络自适应地捕获图形结构并提供同一采集的各种视图以帮助疾病诊断。再次，DCSGBI的建立是为了丰富疾病数据的高层语义信息和低层细节。它为每个分支设计了级联谱图卷积算子，并将不同分支之间的交互策略纳入网络中，成功形成深度模型并捕获来自不同分支的互补信息。通过这种方式，可以学习到更有利且充分的特征以进行可靠的诊断。对多个疾病数据集的实验表明，IDCGN 超越了最先进的模型并取得了有希望的结果。