Jie Yin

Dynamic Heterogeneous Graph Representation Learning: A Survey

This survey presents the first systematic review of DHG representation learning methods and proposes a novel algorithm-centric taxonomy that categorizes existing literature, including early embedding-based approaches, graph neural network (GNN)-based models, and relatively recent Transformer-based DHG methods, while explicitly highlighting their intrinsic modeling biases with respect to dynamic granularity.