Modeling E-mail Networks and Inferring Leadership Using Self-Exciting Point Processes
用自激点过程建模电子邮件网络并推断领导地位
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…