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

在过去的十年中，人们对动态网络数据的收集和分析的兴趣急剧增加。 这种增长与计算资源和互联网等技术发展相一致，可以改进跨时网络数据的测量、收集和建模。 社交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；公司之间的合资企业；以及国家之间的联盟。 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已经允许比过去几十年收集更大、更详细的动态网络数据；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丢失问题，无论是设计原因（例如抽样）还是设计之外（例如机器故障）。 因此，在大型动态网络上收集数据常常会导致数据丢失，这需要新的估计和模拟方法。 该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，并用现实世界的经验案例进行评估。 该项目由多项相互关联的活动组成。 该研究将把当前社交网络数据统计分析中使用的缺失数据技术扩展到有或没有顶点动态的动态网络环境中。 将在多重插补框架下开发几种基于竞争可能性的缺失数据方法。 此外，该研究将通过一系列模拟实验来评估这些缺失数据模型，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的缺失和采样数据方法的当前技术水平。 这些方法将改进对动态社交网络过程（例如在线社交网络、灾难响应网络等）​​的推理和预测，这些问题对于社会学家、统计学家、计算机科学家、人口统计学家、流行病学家和公共政策研究人员来说至关重要。 本研究中使用的测试案例取自广大公众感兴趣的现实世界案例，因此这些方法应该加强危害研究、公共卫生和公共政策领域从业人员的工作。作为博士论文研究改进奖，旨在帮助有前途的学生建立强大的、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.