Hierarchical Models for the Formation and Evolution of Ensembles of Social Networks

社交网络集成的形成和演化的层次模型

• 批准号: 1229271
• 负责人: Brian Junker
• 金额: $ 17万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-15 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1229271&HistoricalAwards=false
• 关键词: Hierarchical; Models; Formation; Evolution; Ensembles

This project deals with the development, testing, and deployment of models for multiple social networks, particularly those with conditionally independent ties. The project will explore their properties with respect to partial pooling across networks, a case that includes a single network or ensemble of networks observed over time. This research is motivated by problems in many sociological fields, particularly education research, in which multiple groups of people form their own networks, including students and teachers alike. The project will build on preliminary constructions of the Hierarchical Latent Space Model and the Hierarchical Mixed-Membership Stochastic Block Model by focusing on how information can be pooled across networks, through hierarchical structure specification, and how model parameters evolve through time, through model-dependent autoregression or other smoothing methods. Multiple ways in which an intervention can affect a subset of these networks also will be studied. These models will use both simulated and real-world data to validate their effectiveness. Standard methods for fitting these models, such as Markov Chain Monte Carlo, will be used initially, though wider deployment of these models will demand the development of quicker inferential procedures based on Variational Inference and/or Sequential Monte Carlo. Finally, model validation will be considered in each of these cases, in terms of comparison to other models as well as the adequacy of a model's fit to data.Data on multiple social networks arising from the same generative mechanisms, and evolving over time together, are becoming increasingly available in education research, public health, and the social sciences. Instead of treating each network separately or assuming that all come from exactly the same model (which is possible only in limited circumstances), this project will provide a new, clearly formulated methodology to deal with this type of data. Researchers in related fields will have the opportunity to use the methods on their own research. Computer code for these routines also will be made available.

该项目涉及多个社交网络的模型的开发、测试和部署，特别是那些具有条件独立关系的社交网络。该项目将探索它们关于跨网络的部分池化的特性，这种情况包括随着时间的推移观察到的单个网络或网络集合。这项研究的动机是许多社会学领域的问题，特别是教育研究，在教育研究中，多个群体形成了自己的网络，包括学生和教师。该项目将建立在分层潜在空间模型和分层混合成员随机区块模型的初步构建的基础上，重点关注如何通过分层结构规范跨网络汇集信息，以及如何通过依赖于模型的自回归或其他平滑方法使模型参数随时间演变。还将研究干预可能影响这些网络的子集的多种方式。这些模型将使用模拟和真实世界的数据来验证其有效性。最初将使用马尔可夫链蒙特卡罗等标准方法来拟合这些模型，尽管这些模型的更广泛部署将要求开发基于变分推理和/或顺序蒙特卡罗的更快的推理程序。最后，将根据与其他模型的比较以及模型与数据的适合性来考虑这些情况下的模型验证。多个社交网络上的数据来自相同的生成机制，并随着时间的推移一起演变，在教育研究、公共卫生和社会科学中越来越多地可用。该项目将提供一种新的、明确制定的方法来处理这类数据，而不是分别对待每个网络或假设所有网络都来自完全相同的模型(只有在有限的情况下才可能)。相关领域的研究人员将有机会将这些方法用于自己的研究。还将提供这些例程的计算机代码。