Abstract
Graphs can be used to model many real-world problems, such as social networks, telecommunication networks and biological structures. To aid the analysis of complex networks, several topological metrics and generational procedures have been proposed in the last years. This work offers a systematic method to analyse different backbone optical networks, based on a non-supervised algorithm for clustering and investigates the power of a recently proposed topological metrics, named \({I({\hat{\mathcal {F}}})}\). The metrics \({I({\hat{\mathcal {F}}})}\) and three others are applied to identify the best canonical model to represent real backbone optical networks. According to the obtained results, the clustering procedure allows to indicate \({I({\hat{\mathcal {F}}})}\) as the better metrics to explain the installed capacity for the analysed networks.
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The authors acknowledge the financial support from CNPq, CAPES, UFPE, UFRPE and UPE.
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de Araújo, D.R.B., Martins-Filho, J.F., Bastos-Filho, C.J.A. (2017). Non-supervised Learning Applied to Analysis of Topological Metrics of Optical Networks. In: Nedjah, N., Lopes, H., Mourelle, L. (eds) Designing with Computational Intelligence. Studies in Computational Intelligence, vol 664. Springer, Cham. https://doi.org/10.1007/978-3-319-44735-3_6
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DOI: https://doi.org/10.1007/978-3-319-44735-3_6
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