  Graph neural networks (GNNs) are the most widely adopted model in
graph-structured data oriented learning and representation. Despite their
extraordinary success in real-world applications, understanding their working
mechanism by theory is still on primary stage. In this paper, we move towards
this goal from the perspective of generalization. To be specific, we first
establish high probability bounds of generalization gap and gradients in
transductive learning with consideration of stochastic optimization. After
that, we provide high probability bounds of generalization gap for popular
GNNs. The theoretical results reveal the architecture specific factors
affecting the generalization gap. Experimental results on benchmark datasets
show the consistency between theoretical results and empirical evidence. Our
results provide new insights in understanding the generalization of GNNs.
