Complex Network Measurements: Estimating the Relevance of Observed Properties Export

@inproceedings{citeulike:3744406, 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.}, author = {Latapy, M. and Magnien, C.}, booktitle = {INFOCOM 2008. The 27th Conference on Computer Communications. IEEE}, citeulike-article-id = {3744406}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/INFOCOM.2008.227}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4509822}, day = {02}, doi = {10.1109/INFOCOM.2008.227}, journal = {INFOCOM 2008. The 27th Conference on Computer Communications. IEEE}, keywords = {complex\_network, complex\_networks, graph\_parameters, measurement\_procedures, modeling, network\_measurements}, month = {May}, pages = {1660--1668}, posted-at = {2009-11-06 12:10:38}, priority = {3}, title = {Complex Network Measurements: Estimating the Relevance of Observed Properties}, url = {http://dx.doi.org/10.1109/INFOCOM.2008.227}, year = {2008} }

TY - CONF ID - citeulike:3744406 L3 - citeulike-article-id:3744406 N2 - 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. JF - INFOCOM 2008. The 27th Conference on Computer Communications. IEEE EP - 1668 TI - Complex Network Measurements: Estimating the Relevance of Observed Properties T2 - INFOCOM 2008. The 27th Conference on Computer Communications. IEEE SP - 1660 KW - complex_network KW - complex_networks KW - graph_parameters KW - measurement_procedures KW - modeling KW - network_measurements AU - Latapy, M AU - Magnien, C PY - 2008/05/02/ UR - http://dx.doi.org/10.1109/INFOCOM.2008.227 ER -