Statistical Models for Dynamic Networks with Endogenous Vertex Migration

该研究项目将开发复杂系统的模型，其中社会实体本身的运动（进入或离开感兴趣的系统或子系统）与这些实体之间的关系内在相关。内源性移徙对于理解重要的社会现象至关重要，这些现象包括组织的招聘和更替，以及灾害发生时志愿人员的大规模聚集。内源性移民是异质性的重要驱动力，可以挑战传统的社会网络结构模型。 这个项目将解决目前的局限性与内源性迁移的网络建模。这些模型的发展将推动复杂社会系统的建模。虽然这项研究的主要影响将是在统计和社会科学界，开发的工具和技术也将适用于生物学，计算机科学和工程的问题。由此产生的见解将对处理紧急反应等重要社会问题的团体或组织具有直接的政策相关性。该项目还将通过学生教育和培训、制作教学材料以及免费提供给政府、产业、研究人员和公众使用的软件工具做出贡献。该项目将开发新的统计模型系列，用于研究具有内生迁移过程的社交网络。 该项目将建立在著名的指数族随机图模型和相关的网络建模框架，以集成迁移过程。 研究人员将开发新的模型类的动态关系数据与内源性迁移和横截面数据所产生的未观察到的迁移依赖的过程。研究人员将使用社交网络数据评估和测试这些新的模型类。各种数据集将被用作新模型的测试平台，包括社交媒体数据、灾害相关数据以及水和聚合物数据。测试平台应用程序将用于评估模型，并促进跨学科结果的交流。通过开发免费的软件工具包和创建培训材料和研讨会，将确保这些模型的广泛使用。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This research project will develop models of complex systems in which the movement of social entities themselves (either into or out of a system of interest or among subsystems) is endogenously related to the relationships among those entities. Endogenous migration is critical to understanding important social phenomena ranging from recruitment into and turnover in organizations to the mass convergence of volunteer responders that occurs when disaster strikes. Endogenous migration is an important driver of the heterogeneity that can challenge conventional models of social network structure. This project will address current limitations in the modeling of networks with endogenous migration. The development of these models will advance modeling of complex social systems. Although the primary impact of this research will be within the statistical and social science communities, the tools and techniques to be developed also will be applicable to problems in biology, computer science, and engineering. The resulting insights will have direct policy relevance for groups or organizations dealing with important societal issues, such as emergency responses. The project also will make contributions via student education and training, the creation of instructional materials, and freely available software tools for use by government, industry, researchers, and the general public.This project will develop new families of statistical models for studying social networks with endogenous migration processes. The project will build on the well-known exponential family random graph model and related network modeling frameworks to integrate migration processes. The investigator will develop of new classes of models for dynamic relational data with endogenous migration and for cross-sectional data arising from unobserved migration-dependent processes. The investigator will evaluate and test these new model classes using social network data. A variety of data sets will be used as testbeds for the new models, including social media data, disaster-related data, and water and polymer data. Testbed applications will be used to evaluate the models and also facilitate the communication of results across disciplines. Broad access to these models will be ensured by the development of freely available software toolkits and the creation of training materials and workshops.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.