RRLFSOR: An Efficient Self-Supervised Learning Strategy of Graph Convolutional Networks
This addresses the problem of data efficiency and over-smoothing in GCNs for researchers and practitioners, though it appears incremental as a novel data augmentation method.
The paper tackles the need for large labeled data and stable adjacency matrices in Graph Convolutional Networks (GCNs) by proposing RRLFSOR, a self-supervised learning strategy that improves performance by up to 21.34% in accuracy on link prediction tasks across three datasets.
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.