BIGDATA: Small: DA: DCM: Measurement and Learning in Large-Scale Social Networks

在社交网络的背景下，“大数据”通常涉及非常大的社会系统的信息，其感兴趣的元素显示复杂的依赖性。 这种系统的最先进的统计模型需要使用计算昂贵的随机模拟技术来捕获这种依赖性，这些技术通常不能很好地扩展到大人口的情况下。 这个问题的一个潜在解决方案是将详细的建模工作集中在较小的亚群上（例如，团体、社区等）从更大的系统中提取。 虽然在这种情况下子系统模型的可扩展性不那么具有挑战性，但是必须具有用于以允许原则性推断的方式从大型网络采样的适当方法，以及识别局部子群体与它们所嵌入的更广泛网络之间的耦合的建模技术。通过在统一的指数族框架内结合机器学习和社交网络建模的专业知识，我们可以解决高度详细的模型和大数据带来的可计算性限制。 该研究将开发新的方法，用于大型社交网络的可扩展测量和分析，通过将其部署在从在线社交网络收集动态数据的背景下来验证这些技术。具体而言，研究人员将联合收割机概率图模型和指数族随机图模型（ERGMs）相结合，以：（i）通过利用有限范围依赖来识别具有低计算要求的模型;（ii）开发用于识别大型网络中弱耦合状态的机器学习技术，以促进采样和子图建模;以及（iii）开发集成的采样和建模策略，用于从大型网络的子图中进行推断，这些子图捕获与它们所嵌入的结构的耦合。 该建议调查这些问题的横截面和动态的上下文中，网络和没有顶点属性。 通过这一项目创造的抽样技术将作为一个更广泛的基础设施的延伸加以部署，以便在由一个主要参与者开发和维护的在线社交网络中收集数据，从而能够在实际环境中进行评估，通过这一研究开发的方法将能够分析与许多公共利益问题有关的数据，包括流行病学、安全和应急管理应用;项目内的数据收集和分析活动将包括自然灾害方面的应用，有可能为灾害期间能够拯救生命和财产的政策提供信息。 该项目将与研究生和本科教育以及博士后指导相结合。 通过这一项目开发的工具将作为广泛使用的开放源码统计网络分析工具包的一部分发布，以便向一系列领域的研究人员和从业人员广泛传播。

In the context of social networks, "big data" generally involves information on very large social systems whose elements of interest display complex dependence. State-of-the-art statistical models for such systems require the use of computationally expensive stochastic simulation techniques to capture this dependence; these techniques do not generally scale well to the large-population case. One potential solution to this problem is to focus detailed modeling efforts on smaller subpopulations (e.g., groups, communities, etc.) extracted from the larger system. While scalability of the subsystem models is less challenging in this case, one must have appropriate methods for sampling from large networks in such a manner as to permit principled inference, and modeling techniques that recognize the coupling between local subpopulations and the broader network in which they are embedded.The PI will bridge the gap between expensive, highly detailed models and the limits of computability imposed by Big Data by combining expertise from machine learning and social network modeling within a unifying exponential family framework. The research will develop novel methods for the scalable measurement and analysis of large social networks, validating these techniques by deploying them in the context of dynamic data collection from online social networks. Specifically, the researchers will combine probabilistic graphical models and exponential family random graph models (ERGMs) to: (i) identify models with low computational requirements by exploiting limited-range dependence; (ii) develop machine learning techniques for identifying weakly coupled regimes in large networks to facilitate sampling and subgraph modeling; and (iii) develop integrated sampling and modeling strategies for inference from subgraphs of large networks that capture coupling to the structures in which they are embedded. This proposal investigates these questions in both the cross-sectional and dynamic contexts, for networks with and without vertex attributes. The sampling techniques created via this project will be deployed as an extension of a broader infrastructure for data collection in online social networks developed and maintained by one of the PIs, allowing for evaluation in a practical setting.The methods developed via this research will allow for analysis of data relating to many problems of public interest, including epidemiological, security, and emergency management applications; data collection and analysis activities within the project will include applications in the natural hazard context, with the potential to inform policies that can save lives and property during disasters. The project will be integrated with graduate and undergraduate education, as well as postdoctoral mentoring. Tools developed via this project will be released as part of a widely used open-source toolkit for statistical network analysis (statnet), allowing widespread dissemination to researchers and practitioners in a range of fields.