Graph or network has been widely used for describing and modeling complex systems in biomedicine. Deep learning methods, especially graph neural networks (GNNs), have been developed to learn and predict with such structured data. In this paper, we proposed a novel transformer and snowball encoding networks (TSEN) for biomedical graph 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 to learn the whole-graph features. On the other hand, TSEN also used snowball graph convolution as position embedding in transformer structure, which was a simple yet effective method for capturing local patterns naturally. Results of experiments using four graph classification datasets demonstrated that TSEN outperformed the state-of-the-art typical GNN models and the graph-transformer based GNN models.

翻译：图或网络已被广泛用于描述和建模生物医学中的复杂系统。深度学习方法，尤其是图神经网络（GNN），已被开发用于学习和预测此类结构化数据。本文提出了一种新颖的Transformer与雪球编码网络（TSEN），用于生物医学图分类，该网络将带有图雪球连接的Transformer架构引入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，增强了捕捉多尺度信息和全局模式以学习全图特征的能力。另一方面，TSEN还采用雪球图卷积作为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。在四个图分类数据集上的实验结果表明，TSEN的性能优于最先进的典型GNN模型以及基于图Transformer的GNN模型。