Graph neural networks (GNNs) have shown great power in learning on attributed
graphs. However, it is still a challenge for GNNs to utilize information
faraway from the source node. Moreover, general GNNs require graph attributes
as input, so they cannot be appled to plain graphs. In the paper, we propose
new models named G-GNNs (Global information for GNNs) to address the above
limitations. First, the global structure and attribute features for each node
are obtained via unsupervised pre-training, which preserve the global
information associated to the node. Then, using the global features and the raw
network attributes, we propose a parallel framework of GNNs to learn different
aspects from these features. The proposed learning methods can be applied to
both plain graphs and attributed graphs. Extensive experiments have shown that
G-GNNs can outperform other state-of-the-art models on three standard
evaluation graphs. Specially, our methods establish new benchmark records on
Cora (84.31\%) and Pubmed (80.95\%) when learning on attributed graphs.

该论文提出了一种名为 G-GNNs 的新型模型，通过无监督预训练获取节点的全局结构和属性特征，然后利用这些特征和原始网络属性，提出了一种 GNN 的并行框架以从这些特征中学习不同的方面。该模型的学习方法可应用于平面图和属性图，并通过 extensive experiments 在三个标准评估图上表现优异，特别地，在属性图学习方面，G-GNNs 在 Cora (84.31%) 和 Pubmed (80.95%) 上建立了新的基准记录。