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). In addition, we also propose another self-supervised learning strategy of GCNs, named randomly removing links with a fixed step at some blocks (RRLFSSB), to solve the problem that adjacent nodes have no selected step. Experiments on transductive link prediction tasks show that our strategies outperform the baseline models consistently by up to 21.34% in terms of accuracy on three benchmark datasets.

翻译：为了进一步提高全球网络的性能和自学能力,我们在本文件中提出了全球网络的高效自我监督学习战略,以随机删除与一个区域固定步骤(RRLFSOR)的链接命名。此外,我们还提出了另一个全球网络的自监督学习战略,以随机删除与某些区块固定步骤(RRLFSSB)的链接,以解决相邻节点没有选定步骤的问题。 关于传输连接预测任务的实验表明,就三个基准数据集的准确性而言,我们的战略比基线模型的精确性一致超过21.34%。