The relational event model (REM) facilitates the study of network evolution in relational event history data, i.e., time-ordered sequences of social interactions. In real-life social networks, however, it is likely that network effects, i.e., the parameters that quantify the relative importance of drivers of these social interaction sequences, change over time. In these networks, the basic REM is not appropriate to understand what drives network evolution. Therefore, this research extends the REM framework with approaches for testing and exploring time-varying network effects. Firstly, we develop a Bayesian approach to test whether network effects change during the study period or not. A simulation study was conducted that illustrates that the Bayesian test accurately quantifies the evidence between a basic (‘static’) REM or a dynamic REM. Secondly, in the case of the latter, time-varying network effects can be studied by means of a moving window that slides over the relational event history. A simulation study was conducted that illustrates that the accuracy and precision of the estimates depend on the window width: narrower windows result in greater accuracy at the cost of lower precision. Thirdly, one challenge of the moving window REM is to determine the window width that can best capture the time-varying network effects. Therefore, we develop a Bayesian approach for determining window widths using the empirical network data. A simulation study was conducted that illustrates that estimation with empirically determined window widths achieves both good accuracy for time intervals with important changes and good precision for time intervals with hardly any changes in the effects. Finally, in an empirical application, it is demonstrated how the approaches in this research can be used to test for and explore time-varying network effects of face-to-face contacts at the workplace.