DHB: Longitudinal Analysis and Modeling of Large-Scale Social Networks Based on Cell Phone Records

DHB：基于手机记录的大规模社交网络的纵向分析和建模

• 批准号: 0826958
• 负责人: Zoltan Toroczkai
• 金额: $ 69.98万
• 依托单位: University of Notre Dame
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-10-01 至 2012-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0826958&HistoricalAwards=false
• 关键词: DHB; Longitudinal; Analysis; Modeling; Large

This project develops novel computational approaches and analytical tools to meet the challenges and opportunities for social network analysis brought by the availability of large-scale longitudinal data generated by the usage patterns of modern communication devices, such as cell-phones. This type of data has several key advantages including the fact that it is statistically extensive (coming from millions of users), purely observational (void of any bias induced by obtrusive measurements), and longitudinal (spanning several years). The extent and longitudinal character of such data brings challenges that can only be tackled by an orchestrated multidisciplinary approach invoking social science, physics methods developed for large-scale interacting particle systems, mathematical statistics and data analysis, and computer science methods for data mining, and agent-based modeling. The project will focus in particular on generating 1) Novel computational and analytic methods for both cross-sectional and longitudinal analysis of large-scale social network data, based on advanced nonlinear time-series methods, community detection algorithms, and probabilistic relational models; 2) Stochastic mathematical models for network behavior coupled across several levels of analysis, including node, dyad, triad and group levels, and 3) A data-driven stochastic individual-based simulation (SIBS) framework with predictive capability for macro-level system behavior, implementing the dynamic models from 2). The SIBS design will allow it to be used as a hypothesis generation multilevel framework for social dynamics, and as an application, it will be employed to uncover the modalities for efficient, targeted spread of information in large-scale dynamic social networks. The suite of methods developed, and the SIBS with its design transparency and parallelism provide data-driven, feature extraction tools for addressing social science questions, as well as aiding mechanism-design and decision-making in practical situations. In particular, they are expected to directly impact applications both within the commercial (product delivery, health-care services, etc.) and non-commercial (urban planning, emergency alert systems, etc.) domains.

该项目开发新颖的计算方法和分析工具，以应对由现代通信设备（如手机）的使用模式产生的大规模纵向数据的可用性所带来的社会网络分析的挑战和机遇。这种类型的数据有几个关键的优势，包括它在统计上是广泛的（来自数百万用户），纯粹是观察性的（没有任何由突发性测量引起的偏差），和纵向的（跨越几年）。这些数据的广度和纵向特征带来了挑战，只能通过协调的多学科方法来解决，这些方法包括社会科学、为大规模相互作用粒子系统开发的物理方法、数学统计和数据分析、数据挖掘和基于代理的建模的计算机科学方法。该项目将特别侧重于：1)基于先进的非线性时间序列方法、社区检测算法和概率关系模型，为大规模社会网络数据的横断面和纵向分析提供新颖的计算和分析方法；2)基于节点、二组、三组和组等多个分析层次的网络行为随机数学模型；3)基于数据驱动的基于个体的随机仿真（SIBS）框架，具有宏观系统行为预测能力，实现了2)的动态模型。SIBS的设计将允许它被用作社会动态的假设生成多层框架，并且作为一个应用程序，它将被用来揭示大规模动态社会网络中有效的、有针对性的信息传播模式。开发的方法套件，以及具有设计透明度和并行性的SIBS，为解决社会科学问题提供了数据驱动的特征提取工具，以及在实际情况下帮助机制设计和决策。特别是，预计它们将直接影响商业（产品交付、保健服务等）和非商业（城市规划、紧急警报系统等）领域的应用。