  Large relational-event history data stemming from large networks are becoming
increasingly available due to recent technological developments (e.g. digital
communication, online databases, etc). This opens many new doors to learning
about complex interaction behavior between actors in temporal social networks.
The relational event model has become the gold standard for relational event
history analysis. Currently, however, the main bottleneck to fit relational
events models is of computational nature in the form of memory storage
limitations and computational complexity. Relational event models are therefore
mainly used for relatively small data sets while larger, more interesting
datasets, including multilevel data structures and relational event data
streams, cannot be analyzed on standard desktop computers. This paper addresses
this problem by developing approximation algorithms based on meta-analysis
methods that can fit relational event models significantly faster while
avoiding the computational issues. In particular, meta-analytic approximations
are proposed for analyzing streams of relational event data and multilevel
relational event data and potentially of combinations thereof. The accuracy and
the statistical properties of the methods are assessed using numerical
simulations. Furthermore, real-world data are used to illustrate the potential
of the methodology to study social interaction behavior in an organizational
network and interaction behavior among political actors. The algorithms are
implemented in a publicly available R package 'remx'.
