  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.
