Temporal graph learning is an emerging area of research in graph representation learning, motivated by the prevalence of evolving and dynamic interconnected data in different domains and applications. In this workshop, which will be the second workshop on temporal graph learning, we plan to bring together researchers working on relevant areas to exchange ideas on different aspects of temporal graph learning including datasets for discrete and continuous time graphs, evaluation strategies, theoretical foundations, as well as using temporal graph learning paradigms in real-world applications.