Transformer and Snowball Graph Convolution Learning for Brain functional network Classification
This work addresses brain disorder diagnosis for medical applications, but it is incremental as it builds on existing GNN and Transformer methods.
The paper tackled brain disorder classification from functional network data by proposing a Transformer and snowball encoding network (TSEN), which outperformed state-of-the-art GNN models on autism and depression datasets.
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.