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

<p>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.</p

Structural Comparison of Cognitive Associative Networks in Two Populations

The cognitive associative structure of 2 populations was studied using network analysis of free-word associations. Structural differences in the associative networks were compared using measures of network centralization, size, density, clustering, and path length. These measures are closely aligned with cognitive theories describing the organization of knowledge and retrieval of concepts from memory. Size and centralization of semantic structures were larger for college students than for 7th graders, while density, clustering, and mean path length were similar. Findings presented reveal that subpopulations might have very different cognitive associative networks. This study suggests that graph theory and network analysis methods are useful in mapping differences in associative structures across groups