Abstract
There are several metrics that are very useful to analyze and to design networks. These metrics, including the spectral-based ones, can be used to retrieve topological properties from the network. We observed that if one applies the Discrete Fourier Transform (DFT) over the eigenvalues of the Laplacian matrix, it is possible to observe different patterns in the DFT depending on some properties of the analyzed networks. In this paper, we propose a new metrics based on the entropy of the DFT samples, that can be used to identify the type of network. We evaluated this metrics in networks generated by four different procedures (k-Regular, Erdos-Renyi, Watts-Strogatz and Barabasi-Albert) and in well-known datasets of real networks. The results indicate that one can use the proposed metrics to identify the generational model of the network.
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de Araújo, D.R.B., Bastos-Filho, C.J.A., Martins-Filho, J.F. (2014). Using the Entropy of the DFT of the Laplacian Eigenvalues to Assess Networks. In: Contucci, P., Menezes, R., Omicini, A., Poncela-Casasnovas, J. (eds) Complex Networks V. Studies in Computational Intelligence, vol 549. Springer, Cham. https://doi.org/10.1007/978-3-319-05401-8_20
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DOI: https://doi.org/10.1007/978-3-319-05401-8_20
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