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Statistical Models for Dynamic Networks with Endogenous Vertex Migration

具有内生顶点迁移的动态网络的统计模型

基本信息

批准号：
1826589
负责人：
Carter Butts
金额：
$ 35万
依托单位：
University of California-Irvine
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-10-01 至 2022-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1826589&HistoricalAwards=false
关键词：
Statistical Models Dynamic Networks Endogenous

项目摘要

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.

这一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在子系统之间)与这些实体之间的关系具有内在联系。内生移徙对于理解重要的社会现象至关重要，这些社会现象从组织的招聘和更替，到灾难来袭时发生的志愿响应人员的大规模汇聚。内生迁移是异质性的重要驱动力，这种异质性可能会挑战传统的社会网络结构模型。该项目将解决目前在内生迁移网络建模方面的局限性。这些模型的发展将推进复杂社会系统的建模。虽然这项研究的主要影响将在统计和社会科学领域内，但将要开发的工具和技术也将适用于生物学、计算机科学和工程学中的问题。由此产生的见解将对处理重要社会问题的团体或组织具有直接的政策相关性，例如紧急反应。该项目还将通过学生教育和培训、创建教学材料以及供政府、行业、研究人员和公众免费使用的软件工具来做出贡献。该项目将开发新的统计模型家族，用于研究具有内生迁移过程的社会网络。该项目将建立在众所周知的指数族随机图模型和相关网络建模框架的基础上，以整合迁移过程。研究人员将为具有内生迁移的动态关系数据和由未观测到的依赖于迁移的过程产生的横截面数据开发新的模型。研究人员将使用社交网络数据评估和测试这些新的模型班级。各种数据集将用作新模型的试验台，包括社交媒体数据、灾难相关数据以及水和聚合物数据。试验台应用程序将用于评估模型，并促进跨学科的结果交流。这些模型的广泛使用将通过开发免费可用的软件工具包以及创建培训材料和工作室来确保。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。