Journal Article

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

Coronges, Kathryn D., Fox, Eric W., Bertozzi, Andrea L., Short, Martin B., Schoenberg, Frederic P.

2016

Overview

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.

• Publisher: Taylor & Francis,Taylor & Francis Group, LLC,Taylor & Francis Ltd
• Subject: Applications and Case Studies / Clustering / Computer mediated communication / Conditional intensity / data collection / e-mail / Email / Enron E-mail dataset / equations / Hawkes process / IkeNet dataset / Instant messaging / Internet / Leadership / Military schools / Motivation / Poisson distribution / Reaction time / Social networks / Statistics