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.

我们提出了一种基于聚合归一化图卷积网络的方法，通过在图采样中利用聚合、跳跃连接和标识映射，将图像和非图像特征融入节点和边缘，从而增强了预测能力，提供了大脑疾病潜在机制的整体视角。实验结果表明，在自闭症谱系障碍和阿尔茨海默病的预测上，相对于图卷积网络，我们的方法在准确度方面分别提高了 50% 和 13.56%。