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Life sciences · Preprint
arXiv · September 4, 2026
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This is a systematic literature survey on dynamic heterogeneous graph representation learning methods, presenting a taxonomy of embedding-based, GNN-based, and Transformer-based approaches. It is a review article summarizing and categorizing existing computational methods, not an original empirical study with quantified clinical or translational outcomes.
Preprint.
Survey introduces unified formal definition for discrete-time and continuous-time DHGs Proposes algorithm-centric taxonomy encompassing embedding-based, GNN-based, and Transformer-based DHG methods Summarizes representative applications, datasets, and benchmarks for DHG representation learning
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This is a literature survey/review article presenting taxonomy and summary of existing methods, not empirical evidence from original research with quantified outcomes or clinical application.
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Graph representation learning (GRL) serves as a canonical paradigm for modeling complex networks. However, real-world AI systems inherently manifest as evolving heterogeneous entities with complex interactions, posing significant challenges to static or homogeneous modeling. To address these complexities, representation learning for Dynamic Heterogeneous Graphs (DHGs) has emerged as a vital approach for learning low-dimensional representations that simultaneously preserve structural semantics and temporal dynamics. This survey presents the first systematic review of DHG representation learning methods. We first introduce a unified formal definition that encompasses both discrete-time and continuous-time DHGs from the perspective of temporal granularity. Building upon this formulation, we propose 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. Furthermore, we summarize representative applications of DHG representation learning, along with commonly used datasets and benchmarks. Finally, we discuss promising research directions that guide future advances in this rapidly evolving field.
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