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

该项目开发了新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。这种类型的数据具有几个关键优势，包括统计广泛（来自数百万用户），纯观察性（没有任何由强迫测量引起的偏差）和纵向（跨越数年）。这些数据的范围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，这些方法涉及社会科学，为大规模相互作用的粒子系统开发的物理方法，数理统计和数据分析，以及用于数据挖掘的计算机科学方法和基于代理的建模。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，为大规模社交网络数据的横截面和纵向分析提供新的计算和分析方法; 2）网络行为的随机数学模型耦合在几个层次的分析，包括节点，二元组，三元组和组水平，以及3）数据驱动的随机基于个体的仿真（SIBS）框架，其具有对宏观级系统行为的预测能力，实现来自2）的动态模型。SIBS的设计将允许它被用来作为一个假设生成多层次的框架，社会动态，并作为一个应用程序，它将被用来揭示的模式，有效的，有针对性的传播信息在大规模的动态社交网络。所开发的一套方法，以及具有设计透明度和并行性的SIBS提供了数据驱动的特征提取工具，用于解决社会科学问题，以及在实际情况下辅助机制设计和决策。特别是，它们预计将直接影响商业（产品交付，医疗保健服务等）和非商业性（城市规划、紧急警报系统等）域.

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.