Detecting events such as major routing changes or congestions in the dynamics of the internet topology is an important but challenging task. We explore here an empirical approach based on a notion of statistically significant events. It consists in identifying properties of graph dynamics which exhibit a homogeneous distribution with outliers, corresponding to events. We apply this approach to ego-centered measurements of the internet topology (views obtained from a single monitor) and show that it succeeds in detecting meaningful events. Finally, we give some hints for the interpretation of detected events in terms of network operations.