  Graph Convolutional Networks (GCNs) are widely used in many applications yet
still need large amounts of labelled data for training. Besides, the adjacency
matrix of GCNs is stable, which makes the data processing strategy cannot
efficiently adjust the quantity of training data from the built graph
structures.To further improve the performance and the self-learning ability of
GCNs,in this paper, we propose an efficient self-supervised learning strategy
of GCNs,named randomly removed links with a fixed step at one region
(RRLFSOR).RRLFSOR can be regarded as a new data augmenter to improve
over-smoothing.RRLFSOR is examined on two efficient and representative GCN
models with three public citation network datasets-Cora,PubMed,and
Citeseer.Experiments on transductive link prediction tasks show that our
strategy outperforms the baseline models consistently by up to 21.34% in terms
of accuracy on three benchmark datasets.
