Structure-adaptive graph neural network with temporal representation and residual connections

Graph learning methods have boosted brain analysis for user healthcare, disease detection, and behavioral modeling. Spatially separated brain regions are functionally connected with different weights, enabling the classification of brain networks from the perspective of graph learning. However, existing methods based on graph neural networks mainly rely on the calculation of node feature correlation and manual threshold selection to obtain the graph structure, which disregards the temporal features of nodes and the latent information in the implicit graph structure. To address this problem, we propose a structure adaptive graph neural network with temporal representation and residual connections (TR-SAGNN) for brain network classification. First, we design a temporal attention learning module to learn the temporal features of the node itself. We design an end-to-end adaptive graph structure learning module based on the product-moment self-attention mechanism, which avoids manual threshold selection and obtains a more accurate graph structure. Second, we design a graph representation learning module based on a residual connection strategy to avoid the problem of insufficient propagation of node features. Last, we design a loss function to consider both the graph classification task and node classification task, which makes the model obtain better graph representation learning ability under the supervision of the node classification label. We conduct extensive experiments on the ANDI dataset. The results show that our model has better end-to-end adaptive graph construction capability as well as feature learning and classification performance.