Deep Learning has become one of the most dominant approaches in Artificial Intelligence research today. Although conventional deep learning techniques have achieved huge successes on Euclidean data such as images, or sequence data such as text, there are many applications that are naturally or best represented with a graph structure. This gap has driven a tide in research for deep learning on graphs, among them Graph Neural Networks (GNNs) are the most successful in coping with various learning tasks across a large number of application domains. In this chapter, we will systematically organize the existing research of GNNs along three axes: foundations, frontiers, and applications. We will introduce the fundamental aspects of GNNs ranging from the popular models and their expressive powers, to the scalability, interpretability and robustness of GNNs. Then, we will discuss various frontier research, ranging from graph classification and link prediction, to graph generation and transformation, graph matching and graph structure learning. Based on them, we further summarize the basic procedures which exploit full use of various GNNs for a large number of applications. Finally, we provide the organization of our book and summarize the roadmap of the various research topics of GNNs.