Adaptive Graph Convolutional Networks based on Decouple and Residuals to relieve Over-Smoothing

For the node classification problem, the traditional graph convolutional network (GCN) and many of its variants achieve the best results at shallow layers, especially for sparse graphs, which do not take full advantage of the higher-order neighbor information of the nodes in the graph. However, multiple stacked graph convolutional networks will suffer from the over-smoothing problem, resulting in ineffective differentiation between different classes of nodes in the graph. To address this problem, we propose an adaptive graph convolutional network based on decouple and residuals (ADR-GCN) to alleviate the over-smoothing problem. The model first obtains the initial representation of nodes using autoencoder and then decouples the representation transformation of nodes and feature propagation. In addition, we add initial residuals to the feature propagation of nodes and adaptively selects the appropriate local and global information of each node to obtain node representations containing rich information. Finally, we use a softmax classifier to generate the final node prediction. The experimental results on seven datasets show that the classification accuracy of ADR-GCN improves 1.3%-4.1% compared with GCN, which shows that the model can better alleviate over-smoothing,