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

University Of Notre Dame, Notre Dame IN

Investigators

Abstract

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.