Modeling e-mail networks and inferring leadership using self-exciting point processes

• Autores: Eric W. Fox, Martin B. Short, Frederic Paik Schoenberg, Kathryn D. Coronges, Andrea L. Bertozzi
• Localización: Journal of the American Statistical Association, ISSN 0162-1459, Vol. 111, Nº 514, 2016, págs. 564-584
• Idioma: inglés
• DOI: 10.1080/01621459.2015.1135802
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• Resumen

  • 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 for fitting 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.