Advanced deep learning methods, especially graph neural networks (GNNs), are increasingly expected to learn from brain functional network data and identify the functional connections between brain disorder and health. 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, 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，特别是图神经网络（GNN），正越来越多地被期望用于从脑功能网络数据中学习，并识别脑疾病与健康之间的功能连接。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，该方法将Transformer架构与图雪球连接引入GNN，以学习全图表示。TSEN通过雪球编码层将图雪球连接与图Transformer相结合，增强了捕捉脑功能网络多尺度信息和全局模式的能力。TSEN还引入了雪球图卷积作为Transformer结构中的位置嵌入，这是一种简单而有效的方法，能够自然地捕捉局部模式。我们使用两个大规模脑功能网络数据集对所提出的模型进行了评估，结果表明TSEN的性能优于最先进的GNN模型以及基于图Transformer的GNN模型。