Doctoral Dissertation Research: Dynamic Network Models for the Scalable Analysis of Networks with Missing or Sampled Joint Edge/Vertex Evolution

在过去十年中，对动态网络数据的收集和分析的兴趣急剧增加。这种增长与技术发展相一致，例如计算资源和因特网，这些技术发展允许改进跨时间网络数据的测量、收集和建模。社会网络的例子包括个人之间的友谊结构、工作线索或紧急信息联系；企业间的合资企业；以及国家间的联盟。通过传感器（例如，手机）、调查和数据库系统进行的现代数据收集使得比过去几十年更大规模、更详细的动态网络数据收集成为可能；然而，即使有了改进的测量工具，仍然存在一个持续存在的数据缺失问题，要么是由于设计（例如，抽样），要么是由于设计（例如，机器故障）。因此，在大型动态网络上收集数据往往会导致数据缺失，这需要新的估计和模拟方法。本博士论文研究项目将采用计算方法、指数族理论和潜在缺失数据框架来开发模型，并将与现实世界的经验案例进行评估。该项目由几个相互联系的活动组成。该研究将扩展目前在社会网络数据统计分析中使用的缺失数据技术到具有或不具有顶点动态的动态网络的背景下。本文提出了几种基于竞争似然的多重插值框架下的缺失数据方法。此外，本研究将通过一系列仿真实验对这些缺失数据模型进行评估，以比较这些缺失数据技术的效率、可扩展性、偏差、准确性和预测准确性。该项目将改进和扩展动态网络模型中缺失和采样数据方法的当前状态。这些方法将允许对动态社会网络过程（例如，在线社会网络，灾难响应网络等）进行改进的推理和预测，这些问题对社会学家，统计学家，计算机科学家，人口学家，流行病学家和公共政策研究人员具有直接重要性。本研究中使用的测试案例来自公众感兴趣的现实世界案例，因此这些方法应该加强从业者在危害研究、公共卫生和公共政策方面的工作。作为博士论文研究改进奖，提供支持，使有前途的学生建立一个强大的，独立的研究生涯。

Interest in the collection and analysis of dynamic network data has increased dramatically over the last decade. This growth coincides with technological developments, such as computational resources and the Internet, that allow for improved measurement, collection, and modeling of inter-temporal network data. Examples of social networks include structures of friendships, job leads, or emergency information ties among individuals; joint ventures among firms; and alliances among nations. Modern data collection through sensors (e.g., cellphones), surveys, and database systems has allowed for larger and more detailed dynamic network data collection than was possible in past decades; however, even with improved measurement tools there exists a persistent problem of missing data, either by design (e.g., sampling) or out of design (e.g., machine failure). Thus, the collection of data on large dynamic networks often results in missing data, which requires new methodology for estimation and simulation. This doctoral dissertation research project will employ computational methods, exponential family theory, and a latent missing data framework to develop models that will be evaluated with real-world empirical cases. The project consists of several linked activities. The research will extend current missing data techniques employed in the statistical analysis of social network data to the context of dynamic networks with and without vertex dynamics. Several competing likelihood-based missing data methods under the framework of multiple imputation will be developed. In addition, the research will evaluate these missing data models through a series of simulation experiments to compare the efficiency, scalability, bias, accuracy, and predictive accuracy of these missing data techniques.This project will improve and extend the current state of the art in missing and sampled data methods for dynamic network models. These methods will allow improved inference and prediction for dynamic social network processes (e.g., online social networks, disaster response networks, etc.), problems of immediate importance to sociologists, statisticians, computer scientists, demographers, epidemiologists, and public policy researchers. The test cases used within this research are drawn from real-world cases of interest to the greater public, so these methods should enhance the work of practitioners in hazards research, public health, and public policy. As a Doctoral Dissertation Research Improvement award, support is provided to enable a promising student to establish a strong, independent research career.