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Detecting events such as major routing changes or congestions in the dynamics of the internet topology is an important but challenging task. The authors explore here a top-down approach based on a notion of statistically significant events. It consists in identifying statistics which exhibit a homogeneous distribution with outliers, which correspond to events. They apply this approach to ego-centerd measurements of the internet topology (views obtained from a single monitor) and show that it succeeds in detecting meaningful events. Finally, they give some hints for the interpretation of such events in terms of network events.
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