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

BIGDATA：小型：DA：DCM：大规模社交网络中的测量和学习

• 批准号: 1251267
• 负责人: Animashree Anandkumar
• 金额: $ 74.68万
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1251267&HistoricalAwards=false
• 关键词: BIGDATA; Small; DA; DCM; Measurement

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.

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，这些系统的兴趣元素表现出复杂的依赖性。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捉这种依赖性；这些技术通常不适用于大规模病例。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从大系统中提取的较小的子种群（例如，群体、社区等）上。虽然在这种情况下，子系统模型的可伸缩性不那么具有挑战性，但必须有适当的方法从大型网络中进行抽样，以便允许有原则的推断，并且必须有识别局部子种群与嵌入它们的更广泛网络之间耦合的建模技术。PI将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，弥合昂贵、高度详细的模型与大数据所带来的可计算性限制之间的差距。该研究将为大型社交网络的可扩展测量和分析开发新的方法，并通过将这些技术部署到在线社交网络的动态数据收集中来验证这些技术。具体而言，研究人员将结合概率图模型和指数族随机图模型（ERGMs）：(i)通过利用有限范围依赖来识别计算需求低的模型；（ii）开发机器学习技术，用于识别大型网络中的弱耦合状态，以促进采样和子图建模；（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，这些子图捕获了与嵌入它们的结构的耦合。本文在横截面和动态环境下，对有顶点属性和没有顶点属性的网络进行了研究。通过该项目创建的抽样技术将作为一个更广泛的基础设施的扩展，用于在线社交网络的数据收集，该基础设施由一个pi开发和维护，允许在实际环境中进行评估。通过这项研究开发的方法将允许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在自然灾害背景下的应用，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源统计网络分析工具包（statnet）的一部分发布，允许广泛传播给一系列领域的研究人员和从业者。