All entities in digital space and the real world?including objects, facts, and human beings?and their relationships can be represented as nodes and edges, leading to large-scale dynamic graphs in graph theory. This talk provide an overview of graph neural networks or GNNs that can learn graph structures and the roles of nodes and edges via neural network, and also introduces target various GNN applications including recommender systems in e-commerce, news platforms,  transportation, materials informatics as well as fraud detection in financial systems that we are working on.