Graph Convolutional Networks (GCNs) are powerful for processing graph-structured data and have achieved state-of-the-art performance in several tasks such as node classification, link prediction, and graph classification. However, it is inevitable for deep GCNs to suffer from an over-smoothing issue that the representations of nodes will tend to be indistinguishable after repeated graph convolution operations. To address this problem, we propose the Graph Partner Neural Network (GPNN) which incorporates a de-parameterized GCN and a parameter-sharing MLP. We provide empirical and theoretical evidence to demonstrate the effectiveness of the proposed MLP partner on tackling over-smoothing while benefiting from appropriate smoothness. To further tackle over-smoothing and regulate the learning process, we introduce a well-designed consistency contrastive loss and KL divergence loss. Besides, we present a graph enhancement technique to improve the overall quality of edges in graphs. While most GCNs can work with shallow architecture only, GPNN can obtain better results through increasing model depth. Experiments on various node classification tasks have demonstrated the state-of-the-art performance of GPNN. Meanwhile, extensive ablation studies are conducted to investigate the contributions of each component in tackling over-smoothing and improving performance.

翻译：为了解决这一问题,我们建议GGNN(GPNN)建立GNN(GPNN),其中包括一个分解的GCN和一个共享参数的MLP。我们提供经验和理论证据,以证明拟议的MLP伙伴在应对过度移动方面的有效性,同时从适当的平稳中获益。为了进一步解决过度移动和规范学习过程,我们引入了一个设计良好的一致性对比损失和KL差异损失。此外,我们提出了一种改善图表总体质量的图表增强技术。大多数GNNN只能与浅层结构合作,但通过提高深度,GPNNN可以取得更好的结果。关于各种节点分类的实验已经展示了NGP业绩的每个部分。