Collaborative Research: Specification and Estimation of Exponential Family Random Graph Models for Weighted Networks

合作研究：加权网络指数族随机图模型的规范和估计

• 批准号: 1357622
• 负责人: Sreekalyani Bhamidi
• 金额: $ 20.27万
• 依托单位: University of North Carolina at Chapel Hill
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-04-15 至 2017-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1357622&HistoricalAwards=false
• 关键词: Collaborative; Research; Specification; Estimation; Exponential

Understanding the effect of network synergies on dynamic relational processes plays an important role in a number of real-world research settings. Examples include understanding what forms of monetary and social policy reduce the instance of international financial contagion, and the role of different physiological conditions on the activity levels of different interconnected regions in the human brain. Statistical insights into areas such as these require analytical methods which deal with both the presence and absence of ties as well as tie strengths between units in networks. This project focuses on the development and implementation of statistical methods and software for the analysis of weighted (i.e., tie strength) network data. The generalized exponential random graph model (GERGM) is a powerful tool for formulating and testing hypotheses about networks. The project will advance the current state of development of the GERGM by (1) developing a better understanding of the space of network probability distributions that can be formulated with the GERGM; developing Markov Chain Monte Carlo methods for estimation, which will broaden the class of GERGM specifications for which estimation is feasible; (3) developing special-case GERGM constraints that facilitate the study of correlation matrices as networks; and (4) developing asymptotic theory regarding the properties of the GERGM family. As part of this research, two illustrative applications of the GERGM will be developed. The first one involves the analysis of global environmental public policy networks, which offers insight into the network properties of global environmental faction and cooperation. The second application involves the analysis of neural activity networks in humans, which aims to understand complex dependencies connecting regions of the brain. Given the recent explosion in the application of statistical network models in fields as diverse as sociology, genetics, neuroscience, political science, physics, finance, linguistics, and ecology, it is expected that the statistical methods developed in this project will be relevant to a number of different fields. One of the leading fields, in terms of the prominence of weighted network data, is neuroscience. One of the aims of this project is to contribute to the multi-agency initiative on Brain Research through Advancing Innovative Neurotechnologies. This project offers two additional contributions that will facilitate the statistical study of weighted networks. First, this project will contribute and disseminate free and open-source statistical software that permits user-friendly applications. Second, the material developed in this project will be incorporated into graduate-level research methods coursework and workshops.

了解网络协同效应对动态关系过程的影响在许多现实世界的研究环境中起着重要的作用。 例如，了解什么形式的货币和社会政策可以减少国际金融传染的情况，以及不同的生理条件对人脑中不同相互关联区域的活动水平的作用。 对这些领域的统计见解需要分析方法，这些方法既要处理关系的存在和不存在，也要处理网络中单元之间的关系强度。 该项目的重点是开发和实施统计方法和软件，用于分析加权（即，联系强度）网络数据。 广义指数随机图模型（GERGM）是一个强有力的工具，制定和测试有关网络的假设。 该项目将通过以下方式推进GERGM的当前发展状况：（1）更好地理解可以用GERGM制定的网络概率分布空间;开发用于估计的马尔可夫链蒙特卡罗方法，这将扩大GERGM规格的类别，使估计可行;（3）发展特殊情况的GERGM约束，促进相关矩阵作为网络的研究;（4）发展关于GERGM族性质的渐近理论。 作为这项研究的一部分，两个说明性的应用程序的GERGM将开发。 第一部分是对全球环境公共政策网络的分析，揭示了全球环境组织与合作的网络特性。 第二个应用涉及对人类神经活动网络的分析，旨在了解连接大脑区域的复杂依赖关系。 鉴于最近统计网络模型在社会学、遗传学、神经科学、政治学、物理学、金融学、语言学和生态学等领域的应用激增，预计本项目中开发的统计方法将与许多不同领域相关。 就加权网络数据的重要性而言，领先的领域之一是神经科学。 该项目的目标之一是通过推进创新神经技术为多机构脑研究倡议做出贡献。 该项目提供了两个额外的贡献，将促进加权网络的统计研究。 第一，该项目将提供和传播免费和开放源码的统计软件，使用户能够方便地使用这些软件。 第二，在这个项目中开发的材料将被纳入研究生水平的研究方法课程和研讨会。