  Temporal network data often encode time-stamped interaction events between
senders and receivers, such as co-authoring a scientific article or sending an
email. A number of relational event frameworks have been proposed to address
specific issues raised by modelling time-stamped data with complex temporal and
spatial dependencies. These models attempt to quantify how individuals'
behaviour, external factors and interaction with other individuals change the
network structure 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, with the
latter thought to represent the inherent complexity of human interaction.
  The propensity to form (or receive) ties can also be related to individual
actors' attributes. Nodal heterogeneity in the network is often modelled by
including actor-specific or dyadic covariates, such as age, gender, shared
neighbourhood, etc. However, capturing personality traits such as popularity or
expansiveness is difficult, if not impossible. A failure to account for
unobserved heterogeneity may confound the substantive effect of key variables
of interest. This research shows how node level popularity in terms of sender
and receiver effects may mask ghost triadic effects. These results suggest that
unobserved nodal heterogeneity plays a substantial role in REM estimation
procedure and influences the conclusions drawn from real-world networks.

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