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上的信息，这些系统的感兴趣的元素表现出复杂的依赖性。这类系统的最新统计模型需要使用计算代价高昂的随机模拟技术来捕捉这种相关性；这些技术通常不能很好地扩展到大人口的情况。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子群体(例如，群体、社区等)上。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，但我们必须拥有适当的方法来从大型网络中进行采样，以允许进行原则性推理，并使用建模技术来识别局部子群和嵌入它们的更广泛网络之间的耦合。PI将通过在统一的指数家庭框架内结合机器学习和社会网络建模的专业知识，弥合昂贵的、高度详细的模型与大数据施加的计算限制之间的差距。这项研究将开发可扩展的大型社交网络测量和分析的新方法，通过将这些技术部署到在线社交网络的动态数据收集环境中来验证这些技术。具体地说，研究人员将结合概率图形模型和指数族随机图模型(ERGM)来：(I)通过利用有限范围相关性来识别具有低计算要求的模型；(Ii)开发机器学习技术来识别大型网络中的弱耦合状态，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推断与其嵌入的结构的耦合。该方案在具有和不具有顶点属性的网络的横截面和动态环境中研究了这些问题。通过该项目创建的抽样技术将被部署为在线社交网络中数据收集的更广泛基础设施的扩展，该基础设施由一个私人投资机构开发和维护，允许在实际环境中进行评估。通过该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、安全和紧急管理应用程序；项目内的数据收集和分析活动将包括自然灾害背景下的应用程序，有可能为在灾害期间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生教育以及博士后指导相结合。通过该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(STATNET)的一部分发布，以便广泛传播给一系列领域的研究人员和从业人员。

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.