  Graph Convolutional Networks (GCNs) have been successfully applied to analyze
non-grid data, where the classical convolutional neural networks (CNNs) cannot
be directly used. One similarity shared by GCNs and CNNs is the requirement of
massive amount of labeled data for network training. In addition, GCNs need the
adjacency matrix as input to define the relationship between those non-grid
data, which leads to all of data including training, validation and test data
typically forms only one graph structures data for training. Furthermore, the
adjacency matrix is usually pre-defined and stationary, which makes the data
augmentation strategies cannot be employed on the constructed graph structures
data to augment the amount of training data. To further improve the learning
capacity and model performance under the limited training data, in this paper,
we propose two types of self-supervised learning strategies to exploit
available information from the input graph structure data itself. Our proposed
self-supervised learning strategies are examined on two representative GCN
models with three public citation network datasets - Citeseer, Cora and Pubmed.
The experimental results demonstrate the generalization ability as well as the
portability of our proposed strategies, which can significantly improve the
performance of GCNs with the power of self-supervised learning in improving
feature learning.
