  Graph neural networks (GNNs) have been widely used for representation
learning on graph data. However, there is limited understanding on how much
performance GNNs actually gain from graph data. This paper introduces a
context-surrounding GNN framework and proposes two smoothness metrics to
measure the quantity and quality of information obtained from graph data. A new
GNN model, called CS-GNN, is then designed to improve the use of graph
information based on the smoothness values of a graph. CS-GNN is shown to
achieve better performance than existing methods in different types of real
graphs.
