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Complex Network Measurements: Estimating the Relevance of Observed Properties

Matthieu Latapy 1 Clémence Magnien 1
1 NPA - Networks and Performance Analysis
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : Complex networks, modeled as large graphs, received much attention during these last years. However, topological information on these networks is only available through intricate measurement procedures. Until recently, most studies assumed that these procedures eventually lead to samples large enough to be representative of the whole, at least concerning some key properties. This has a crucial impact on network modeling and simulation, which rely on these properties. Recent contributions proved that this assumption may be misleading, but no solution has been proposed. We provide here the first practical methodology to distinguish between cases where it is indeed misleading, and cases where the observed properties may be trusted. It consists in studying how the properties of interest evolve when the sample grows, and in particular whether they reach a steady state or not. In order to illustrate this method and to demonstrate its relevance, we apply it to data-sets on complex network measurements that are representative of the ones commonly used. The obtained results show that the method fulfills its goals very well. We moreover identify some properties which seem easier to evaluate in practice, thus opening interesting perspectives.
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Submitted on : Monday, April 11, 2016 - 3:06:29 PM
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Matthieu Latapy, Clémence Magnien. Complex Network Measurements: Estimating the Relevance of Observed Properties. IEEE INFOCOM, Apr 2008, Phoenix, United States. pp.2333-2341, ⟨10.1109/INFOCOM.2008.227⟩. ⟨hal-01300902⟩



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