Advanced deep learning methods, especially graph neural networks (GNNs), are increasingly expected to learn from brain functional network data and predict brain disorders. In this paper, we proposed a novel Transformer and snowball encoding networks (TSEN) for brain functional network classification, which introduced Transformer architecture with graph snowball connection into GNNs for learning whole-graph representation. TSEN combined graph snowball connection with graph Transformer by snowball encoding layers, which enhanced the power to capture multi-scale information and global patterns of brain functional networks. TSEN also introduced snowball graph convolution as position embedding in Transformer structure, which was a simple yet effective method for capturing local patterns naturally. We evaluated the proposed model by two large-scale brain functional network datasets from autism spectrum disorder and major depressive disorder respectively, and the results demonstrated that TSEN outperformed the state-of-the-art GNN models and the graph-transformer based GNN models.

翻译：先进的深度学习方法，尤其是图神经网络，正日益被期望用于学习脑功能网络数据并预测脑部疾病。本文提出了一种新颖的Transformer与雪球编码网络（TSEN），用于脑功能网络分类，该网络将带有图表雪球连接的Transformer架构引入图神经网络，以学习整个图的表示。TSEN通过雪球编码层将图表雪球连接与图Transformer相结合，增强了捕获脑功能网络多尺度信息和全局模式的能力。TSEN还将雪球图卷积作为Transformer结构中的位置嵌入，这是一种简单而有效的方法，能够自然捕获局部模式。我们利用来自自闭症谱系障碍和重度抑郁症的两个大规模脑功能网络数据集对所提模型进行了评估，结果表明TSEN优于最先进的图神经网络模型和基于图Transformer的图神经网络模型。