Protecting medical privacy can create obstacles in the analysis and distribution of healthcare graphs and statistical inferences accompanying them. We pose a graph simulation model which generates networks using degree and property augmentation (GRANDPA) and provide a flexible R package that allows users to create graphs that preserve vertex attribute relationships and approximating retaining topological properties observed in the original graph (e.g., community structure). We support our proposed algorithm using a case study based on Zachary's karate network and a patient-sharing graph generated from Medicare claims data in 2019. In both cases, we find that community structure is preserved, and normalized root mean square error between cumulative distributions of the degrees is low (0.0508 and 0.0514 respectively).

翻译：保护医疗隐私在分析和分配保健图表和随附的统计推论方面会造成障碍,我们提出一个图形模拟模型,利用程度和财产增强(GRANDPA)生成网络,并提供灵活的R软件包,使用户能够创建图表,保存顶层属性关系,并保持原始图表(例如社区结构)所观察到的相似的地形属性。我们支持我们提议的算法,根据Zachary的空手道网络进行案例研究,并根据2019年Medicare索赔数据生成的病人共享图。在这两种情况下,我们发现社区结构都得到了维护,而不同度累计分布之间的正常根正方差(分别为0.0508和0.0514)较低。