Relational hyperevent models for polyadic interaction networks
针对一对多（多元）社交交互网络，提出关系超事件模型（RHEM），将事件率定义为发送者与整个接收者集合的超边协变量的函数，并在Enron邮件数据集上展示其经验价值。
Abstract Polyadic, or ‘multicast’ social interaction networks arise when one sender addresses multiple receivers simultaneously. Available relational event models are not well suited to the analysis of polyadic interaction networks because they specify event rates for sets of receivers as functions of dyadic covariates associated with the sender and one receiver at a time. Relational hyperevent models (RHEM) address this problem by specifying event rates as functions of hyperedge covariates associated with the sender and the entire set of receivers. We illustrate the empirical value of RHEM in a comparative reanalysis of the canonical Enron email data set.