Advances in information technology have increased the availability of time-stamped
relational data, such as those produced by email exchanges or interaction through social …

Relational event models expand the analytical possibilities of existing statistical models for
interorganizational networks by:(i) making efficient use of information contained in the …

Dynamic Network Actor Models (DyNAMs) assume that an observed sequence of relational
events is the outcome of an actor-oriented decision process consisting of two decision …

We introduce a novel procedure to assess the goodness of fit in relational event models.
Building on existing auxiliary variable approaches developed in network modelling, the …

Large relational-event history data stemming from large networks are becoming increasingly
available due to recent technological developments (eg digital communication, online …

Relational event models shift the analytical focus away from network ties defined in terms of
transitions between mutually exclusive states of connectivity, to bonding processes …

Relational event models (REMs) are the primary choice for the analysis of relational-event
network data. However, the standard REM assumes static parameters, which hinders the …

We study the effect of generalized exchange as a possible mechanism reproducing the
flows of resources among participants in financial markets. In an analysis of on-line trading …

As a prominent application of deep neural networks in financial literature, bank credit ratings
play a pivotal role in safeguarding global economic stability and preventing crises. In the …

Dynamic networks offer an insight of how relational systems evolve. However, modeling
these networks efficiently remains a challenge, primarily due to computational constraints …