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Abstract:
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"Information about real-world networks is often characterized by incompleteness and noise, which are consequences of the lack of complete data as well as artifacts in the acquisition process. Because the characterization, analysis and modeling of complex systems underlain by complex networks are critically affected by the quality of the respective structures, it becomes imperative not only to improve the quality of data, but also to devise methodologies for identifying and quantifying the effect of such sampling problems on the characterization of complex networks. In this article we report such a study, involving 10 different measurements, 4 complex networks models and 5 real world networks. We evaluate the sensitiveness of the measurements to perturbations in the topology of the network. Three particularly important types of progressive perturbations to the network are considered: edge suppression, addition and rewiring. The obtained results allowed conclusions with important practical consequences including the identification that edge removal is less critical than rewiring, followed by edge addition. The measurements allowing a better balance of stability (smaller sensitivity to perturbations) and discriminability (possibility of identification of different network topologies) were also identified."
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