Cross-View Graph Consistency Learning for Invariant Graph
  Representations
Jie Chen Zhiming Li Hua Mao Wai Lok Woo Xi Peng
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.