I will give a high-level overview of graph representation learning that uses homomorphism counting in one way or the other. Homomorphism counting is a way to measure 'how often' a given pattern appears in a graph. In the context of (message passing) graph neural networks (GNNs), homomorphism counting is mainly used in two areas: (1) Quantifying what GNNs can do and where they fail and (2) improving the capabilities of GNNs. Special interest in both these areas lies on the expressivity of GNNs, i.e., their ability to learn different representations for nonisomorphic graphs. I will present and discuss the basics, as well as recent results in this interesting and active area of research.