Node-Smoothness-Based Adaptive Initial Residual Deep Graph Convolutional Network

Deep graph convolutional networks can mine the deeper information of non-structured data, e.g., capturing complex interactions within sensor topology. However, the over-smoothing problem severely limits the depth of the graph convolutional network (GCN). The initial residual can ensure the nodes retain some initial information during the propagation process, which largely alleviates the over-smoothing problem in deep graph convolutional networks. However, current works only use the grid search method to determine a fixed initial residual ratio, which can not assign the most appropriate initial information for the nodes with different over-smoothnesses. This paper proposes a novel method named Node-Smoothness Based Adaptive Initial Residual Deep Graph Convolutional Network (NSAIR-GCN). Specifically, it can be divided into two processes: (1) considering the over-smoothing from another perspective, i.e., determining whether nodes are over-smoothed based on the difference in node representations before and after updating and identifying those severely over-smoothed nodes; (2) assigning appropriate initial residual ratios to these nodes based on their smoothness. Extensive semi-supervised node classification experiments on several standard datasets have shown that the adaptive initial residual ratio determined by node smoothness performs better than the previous fixed initial residual ratio and achieves the state-of-the-art.