Over-Smoothing Algorithm and Its Application to GCN Semi-supervised Classification

The feature information of the local graph structure and the nodes may be over-smoothing due to the large number of encodings, which causes the node characterization to converge to one or several values. In other words, nodes from different clusters become difficult to distinguish, as two different classes of nodes with closer topological distance are more likely to belong to the same class and vice versa. To alleviate this problem, an over-smoothing algorithm is proposed, and a method of reweighted mechanism is applied to make the trade-off of the information representation of nodes and neighborhoods more reasonable. By improving several propagation models, including Chebyshev polynomial kernel model and Laplace linear 1st Chebyshev kernel model, a new model named RWGCN based on different propagation kernels was proposed logically. The experiments show that satisfactory results are achieved on the semi-supervised classification task of graph type data.