Heterogeneous knowledge graphs are emerging as an abstraction to represent complex data, such as social networks, knowledge graphs, molecular graphs, biomedical networks, as well as for modeling 3D objects, manifolds, and source code. AI, especially deep representation learning, on graphs is an emerging field with a wide array of applications from protein folding and fraud detection, to drug discovery and recommender systems. In this talk I will discuss recent methodological advancements that automatically learn to encode graph structure into low-dimensional embeddings. I will also discuss industrial applications, software frameworks, benchmarks, and challenges with scaling-up graph learning systems.