Graph neural networks (GNNs) research has surged to become one of the hottest topics in machine learning in the last years. GNNs have seen a series of recent successes in problems from the fields of biology, chemistry, social science, physics, and many others. So far, GNN models have been primarily developed for static graphs that do not change over time. However, many interesting real-world graphs are dynamic and evolving in time, with prominent examples including social networks, financial transactions, and recommender systems. In many cases, it is the dynamic behavior of such systems that conveys important insights, otherwise lost if one considers only a static graph. This talk will discuss Temporal Graph Networks, a recent and general approach for machine learning over dynamic graphs.