While graph convolution based methods have become the de-facto standard for graph representation learning, their applications to disease prediction tasks remain quite limited, particularly in the classification of neurodevelopmental and neurodegenerative brain disorders. In this paper, we introduce an aggregator normalization graph convolutional network by leveraging aggregation in graph sampling, as well as skip connections and identity mapping. The proposed model learns discriminative graph node representations by incorporating both imaging and non-imaging features into the graph nodes and edges, respectively, with the aim of augmenting predictive capabilities and providing a holistic perspective on the underlying mechanisms of brain disorders. Skip connections enable the direct flow of information from the input features to later layers of the network, while identity mapping helps maintain the structural information of the graph during feature learning. We benchmark our model against several recent baseline methods on two large datasets, Autism Brain Imaging Data Exchange (ABIDE) and Alzheimer's Disease Neuroimaging Initiative (ADNI), for the prediction of autism spectrum disorder and Alzheimer's disease, respectively. Experimental results demonstrate the competitive performance of our approach in comparison with recent baselines in terms of several evaluation metrics, achieving relative improvements of 50% and 13.56% in classification accuracy over graph convolutional networks on ABIDE and ADNI, respectively.

翻译：尽管基于图卷积的方法已成为图表示学习的事实标准，但其在疾病预测任务中的应用仍然相当有限，特别是在神经发育性和神经退行性脑部疾病的分类中。本文通过在图采样中利用聚合机制，结合跳跃连接和恒等映射，引入了一种聚合器归一化图卷积网络。所提出的模型分别将影像和非影像特征融入图节点和边中，学习具有判别性的图节点表示，旨在增强预测能力并为脑部疾病潜在机制提供整体视角。跳跃连接允许信息从输入特征直接流向网络的后续层，而恒等映射有助于在特征学习过程中保持图的结构信息。我们在两个大型数据集——孤独症脑成像数据交换库（ABIDE）和阿尔茨海默病神经影像学倡议（ADNI）上，分别针对孤独症谱系障碍和阿尔茨海默病的预测，将我们的模型与几种近期基线方法进行了基准测试。实验结果表明，在多项评估指标上，我们的方法与近期基线相比具有竞争力的性能，在ABIDE和ADNI数据集上，分类准确率相较于图卷积网络分别实现了50%和13.56%的相对提升。