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.

本文提出了两种无监督自学习策略来利用输入图形结构数据中的可用信息，以进一步改善有限的训练数据下模型的学习能力和性能。该方法在三个公共引文网络数据集上使用两个代表性的 GCN 模型进行了实验验证，展示了其在显著提高特征学习方面的自我监督学习能力和可移植性。

自监督训练的图卷积网络

Self-supervised Training of Graph Convolutional Networks

Most previous work on neural text generation from graph-structured data
relies on standard sequence-to-sequence methods. These approaches linearise the
input graph to be fed to a recurrent neural network. In this paper, we propose
an alternative encoder based on graph convolutional networks that directly
exploits the input structure. We report results on two graph-to-sequence
datasets that empirically show the benefits of explicitly encoding the input
graph structure.

本文提出一种基于图卷积网络的替代编码器来直接利用输入文本结构，取代了传统基于序列到序列的方法，实证显示显式编码输入图结构的好处。