Modern data-driven applications increasingly rely on large, heterogeneous datasets collected across multiple sites. Differences in data availability, feature representation, and underlying populations often induce structured missingness, complicating efforts to transfer information from data-rich settings to those with limited data. Many transfer learning methods overlook this structure, limiting their ability to capture meaningful relationships across sites. We propose TransNEST (Transfer learning with Network Embeddings under STructured missingness), a framework that integrates graphical data from source and target sites with prior group structure to construct and refine network embeddings. TransNEST accommodates site-specific features, captures within-group heterogeneity and between-site differences adaptively, and improves embedding estimation under partial feature overlap. We establish the convergence rate for the TransNEST estimator and demonstrate strong finite-sample performance in simulations. We apply TransNEST to a multi-site electronic health record study, transferring feature embeddings from a general hospital system to a pediatric hospital system. Using a hierarchical ontology structure, TransNEST improves pediatric embeddings and supports more accurate pediatric knowledge extraction, achieving the best accuracy for identifying pediatric-specific relational feature pairs compared with benchmark methods.

翻译：现代数据驱动应用日益依赖于跨多个站点收集的大型异构数据集。数据可用性、特征表示和基础人群的差异常常导致结构化缺失，使得将信息从数据丰富的环境迁移到数据有限的环境变得复杂。许多迁移学习方法忽视了这种结构，限制了其捕捉跨站点有意义关系的能力。我们提出了TransNEST（基于结构化缺失的网络嵌入迁移学习），该框架整合了源站点和目标站点的图数据以及先验群体结构，以构建和优化网络嵌入。TransNEST能够适应站点特定特征，自适应地捕捉组内异质性和站点间差异，并在部分特征重叠的情况下改进嵌入估计。我们建立了TransNEST估计量的收敛速率，并在模拟中展示了其优异的有限样本性能。我们将TransNEST应用于一项多站点电子健康记录研究，将特征嵌入从综合医院系统迁移至儿科医院系统。利用分层本体结构，TransNEST改进了儿科特征嵌入，支持更准确的儿科知识提取，在识别儿科特定关系特征对方面相比基准方法取得了最佳准确率。