What are the Best Hierarchical Descriptors for Complex Networks?

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dc.creatorCosta, Luciano da F.
dc.creatorAndrade, Roberto F. S.
dc.date2007-05-29
dc.date.accessioned2026-07-07T08:30:15Z
dc.date.available2026-07-07T08:30:15Z
dc.descriptionThis work reviews several hierarchical measurements of the topology of complex networks and then applies feature selection concepts and methods in order to quantify the relative importance of each measurement with respect to the discrimination between four representative theoretical network models, namely Erdös-Rényi, Barabási-Albert, Watts-Strogatz as well as a geographical type of network. The obtained results confirmed that the four models can be well-separated by using a combination of measurements. In addition, the relative contribution of each considered feature for the overall discrimination of the models was quantified in terms of the respective weights in the canonical projection into two dimensions, with the traditional clustering coefficient, hierarchical clustering coefficient and neighborhood clustering coefficient resulting particularly effective. Interestingly, the average shortest path length and hierarchical node degrees contributed little for the separation of the four network models.
dc.description9 pages, 4 figures
dc.identifierhttps://arxiv.org/abs/0705.4251
dc.identifierhttp://arxiv.org/abs/0705.4251
dc.identifierNew J. Phys. 9 (2007) 311
dc.identifierdoi:10.1088/1367-2630/9/9/311
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/138194
dc.subjectDisordered Systems and Neural Networks
dc.subjectStatistical Mechanics
dc.titleWhat are the Best Hierarchical Descriptors for Complex Networks?
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