Graph representation learning is fundamental for analyzing graph-structured data. Exploring invariant graph representations remains a challenge for most existing graph representation learning methods. In this paper, we propose a cross-view graph consistency learning (CGCL) method that learns invariant graph representations for link prediction. First, two complementary augmented views are derived from an incomplete graph structure through a bidirectional graph structure augmentation scheme. This augmentation scheme mitigates the potential information loss that is commonly associated with various data augmentation techniques involving raw graph data, such as edge perturbation, node removal, and attribute masking. Second, we propose a CGCL model that can learn invariant graph representations. A cross-view training scheme is proposed to train the proposed CGCL model. This scheme attempts to maximize the consistency information between one augmented view and the graph structure reconstructed from the other augmented view. Furthermore, we offer a comprehensive theoretical CGCL analysis. This paper empirically and experimentally demonstrates the effectiveness of the proposed CGCL method, achieving competitive results on graph datasets in comparisons with several state-of-the-art algorithms.

翻译：图表示学习是分析图结构数据的基础。探索不变图表示仍是大多数现有图表示学习方法的挑战。本文提出了一种跨视图图一致性学习（CGCL）方法，用于学习链接预测中的不变图表示。首先，通过双向图结构增强方案从不完整的图结构中生成两个互补的增强视图。该增强方案缓解了各种涉及原始图数据的数据增强技术（如边扰动、节点删除和属性掩码）中常见的信息潜在损失。其次，我们提出了一种能够学习不变图表示的CGCL模型，并提出了一种跨视图训练方案来训练该模型。该方案旨在最大化一个增强视图与另一个增强视图重构的图结构之间的一致性信息。此外，我们还提供了全面的CGCL理论分析。通过实验和实证研究，本文证明了所提出的CGCL方法的有效性，在与多种最先进算法的比较中，在图数据集上取得了具有竞争力的结果。