Directional relational event data, such as email data, often include multiple receivers for each event. Statistical methods for adequately modeling such data are limited however. In this article, a multiplicative latent factor model is proposed for relational event data with multiple receivers. For a given event (or message) all potential receiver actors are given a suitability score. When this score exceeds a sender-specific threshold value, the actor is added to the receiver set. The suitability score of a receiver actor for a given message can depend on observed sender and receiver specific characteristics, and on the latent variables of the sender, of the receiver, and of the message. One way to view these latent variables as the degree of specific unobserved topics on which an actor can be active as sender, as receiver, or that are relevant for a given message. Bayesian estimation of the model is relatively straightforward due to the Gaussian distribution of the latent suitability scale. The applicability of the model is illustrated on simulated data and on Enron email data for which about a third of the messages have at least two receivers.
翻译:方向关系事件数据,如电子邮件数据,通常包括每个事件的多个接收者。适当模拟这些数据的统计方法有限。在本篇文章中,为多个接收者的关系事件数据提议了一个多复制的潜在要素模型。对于一个特定事件(或电文),所有潜在接收者行为者都得到一个适合性的评分。当这一评分超过发送者特有的临界值时,将行为者添加到接收器组中。接收者对给定信息的适当性评分可取决于已观察到的发送者和接收者的具体特点,以及发送者、接收者和电文的潜在变量。将这些潜在变量视为一个行为者作为发送者、作为接收者或与给定信息相关的特定未观测主题的程度的一种方式。由于潜在适配度尺度的分布是高斯语,因此对模型的估算相对简单。模型的适用性在模拟数据上和Enron电子邮件数据上作了说明,其中约三分之一的信息至少有两个接收者。