Modeling E-mail Networks and Inferring Leadership Using Self-Exciting Point Processes

• 成果类型: Article
• 署名作者: Fox, Eric W.; Short, Martin B.; Schoenberg, Frederic P.; Coronges, Kathryn D.; Bertozzi, Andrea L.
• 署名单位: University of California System; University of California Los Angeles
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2015.1135802
• 发表日期: 2016
• 页码: 564-584
• 关键词: Heavy tails

摘要

We propose various self-exciting point process models for the times when e-mails are sent between individuals in a social network. Using an expectation maximization (EM)-type approach, we fit these models to an e-mail network dataset from West Point Military Academy and the Enron e-mail dataset. We argue that the self-exciting models adequately capture major temporal clustering features in the data and perform better than traditional stationary Poisson models. We also investigate how accounting for diurnal and weekly trends in e-mail activity improves the overall fit to the observed network data. A motivation and application forfitting these self-exciting models is to use parameter estimates to characterize important e-mail communication behaviors such as the baseline sending rates, average reply rates, and average response times. A primary goal is to use these features, estimated from the self-exciting models, to infer the underlying leadership status of users in the West Point and Enron networks. Supplementary materials for this article are available online.