Tag Archives: statistical analysis

Papers

Detecting Events in the Dynamics of Ego-centered Measurements of the Internet Topology

Assia Hamzaoui, Matthieu Latapy and Clémence Magnien

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 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. We 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, we give some hints for the interpretation of such events in terms of network events.

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Plots

Event detection around me

Event detection around me

> By Assia Hamzaoui and Matthieu Latapy In the same spirit as in the plot named Dynamics of IP addresses around me, we use egocentric measurements of the Internet. We seek to observe and understand the dynamics of the IP internet topology by detecting events in it. A natural way to see events would be […]

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Papers

Description and simulation of dynamic mobility networks

Pierre Borgnat, Eric Fleury, Jean-Loup Guillaume, Céline Robardet and Antoine Scherrer

During the last decade, the study of large scale complex networks has attracted a substantial amount of attention and works from several domains: sociology, biology, computer science, epidemiology. Most of such complex networks are inherently dynamic, with new vertices and links appearing while some old ones disappear. Until recently, the dynamic of these networks was less studied and there is a strong need for dynamic network models in order to sustain protocol performance evaluations and fundamental analyzes in all the research domains listed above. We propose in this paper a novel framework for the study of dynamic mobility networks. We address the characterization of dynamics by proposing an in-depth description and analysis of two real-world data sets. We show in particular that links creation and deletion processes are independent of other graph properties and that such networks exhibit a large number of possible configurations, from sparse to dense. From those observations, we propose simple yet very accurate models that allow to generate random mobility graphs with similar temporal behavior as the one observed in experimental data.

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