Temporal network data is often encoded as time-stamped interaction events between senders and receivers, such as co-authoring scientific articles or communication via email. A number of relational event frameworks have been proposed to address specific issues raised by complex temporal dependencies. These models attempt to quantify how individual behaviour, endogenous and exogenous factors, as well as interactions with other individuals modify the network dynamics over time. It is often of interest to determine whether changes in the network can be attributed to endogenous mechanisms reflecting natural relational tendencies, such as reciprocity or triadic effects. The propensity to form or receive ties can also, at least partially, be related to actor attributes. Nodal heterogeneity in the network is often modelled by including actor-specific or dyadic covariates. However, comprehensively capturing all personality traits is difficult in practice, if not impossible. A failure to account for heterogeneity may confound the substantive effect of key variables of interest. This work shows that failing to account for node level sender and receiver effects can induce ghost triadic effects. We propose a random-effect extension of the relational event model to deal with these problems. We show that it is often effective over more traditional approaches, such as in-degree and out-degree statistics. These results that the violation of the hierarchy principle due to insufficient information about nodal heterogeneity can be resolved by including random effects in the relational event model as a standard.

翻译：时序网络数据常被编码为发送者与接收者之间带有时间戳的交互事件，例如合著科学论文或通过电子邮件进行通信。为应对复杂时间依赖性引发的特定问题，学界已提出多种关系事件框架。这些模型试图量化个体行为、内生因素与外生因素，以及个体间互动如何随时间改变网络动态。通常需要判断网络变化是否可归因于反映自然关系倾向的内生机制（如互惠性或三元效应）。形成或接收联系的倾向性至少部分可能与行为者属性相关。网络中的节点异质性通常通过纳入行为者特定或二元协变量进行建模。然而在实践中，全面捕捉所有人格特质即使并非不可能，也极具挑战性。未能充分考虑异质性可能混淆关键变量的实质性效应。本研究表明，未能考虑节点层面的发送者和接收者效应可能诱发虚假三元效应。我们提出一种关系事件模型的随机效应扩展以应对这些问题，并证明该方法通常优于入度和出度统计等传统方法。这些结果表明，因节点异质性信息不足导致的层级原则违反可通过将随机效应作为标准纳入关系事件模型得到解决。