Outskewer: Using Skewness to Spot Outliers in Samples and Time Series

Sébastien Heymann 1 Matthieu Latapy 1 Clémence Magnien 1
1 ComplexNetworks
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : Finding outliers in datasets is a classical problem of high interest for (dynamic) social network analysis. However, most methods rely on assumptions which are rarely met in practice, such as prior knowledge of some outliers or about normal behavior. We propose here Outskewer, a new approach based on the notion of skewness (a measure of the symmetry of a distribution) and its evolution when extremal values are removed one by one. Our method is easy to set up, it requires no prior knowledge on the system, and it may be used on-line. We illustrate its performance on two data sets representative of many use-cases: evolution of ego-centered views of the internet topology, and logs of queries entered into a search engine.
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Sébastien Heymann, Matthieu Latapy, Clémence Magnien. Outskewer: Using Skewness to Spot Outliers in Samples and Time Series. ASONAM 2012 - IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Aug 2012, Istanbul, Turkey. pp.527-534, ⟨10.1109/ASONAM.2012.91⟩. ⟨hal-00700465⟩

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