  Graph neural network (GNN) has shown convincing performance in learning
powerful node representations that preserve both node attributes and graph
structural information. However, many GNNs encounter problems in effectiveness
and efficiency when they are designed with a deeper network structure or handle
large-sized graphs. Several sampling algorithms have been proposed for
improving and accelerating the training of GNNs, yet they ignore understanding
the source of GNN performance gain. The measurement of information within graph
data can help the sampling algorithms to keep high-value information while
removing redundant information and even noise. In this paper, we propose a
Metric-Guided (MeGuide) subgraph learning framework for GNNs. MeGuide employs
two novel metrics: Feature Smoothness and Connection Failure Distance to guide
the subgraph sampling and mini-batch based training. Feature Smoothness is
designed for analyzing the feature of nodes in order to retain the most
valuable information, while Connection Failure Distance can measure the
structural information to control the size of subgraphs. We demonstrate the
effectiveness and efficiency of MeGuide in training various GNNs on multiple
datasets.
