  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.
