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      Characterization and detection of thermoacoustic combustion oscillations based on statistical complexity and complex-network theory

      , , ,
      Physical Review E
      American Physical Society (APS)

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          Fast unfolding of communities in large networks

          We propose a simple method to extract the community structure of large networks. Our method is a heuristic method that is based on modularity optimization. It is shown to outperform all other known community detection method in terms of computation time. Moreover, the quality of the communities detected is very good, as measured by the so-called modularity. This is shown first by identifying language communities in a Belgian mobile phone network of 2.6 million customers and by analyzing a web graph of 118 million nodes and more than one billion links. The accuracy of our algorithm is also verified on ad-hoc modular networks. .
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            Characterization of Strange Attractors

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              Permutation Entropy: A Natural Complexity Measure for Time Series

              We introduce complexity parameters for time series based on comparison of neighboring values. The definition directly applies to arbitrary real-world data. For some well-known chaotic dynamical systems it is shown that our complexity behaves similar to Lyapunov exponents, and is particularly useful in the presence of dynamical or observational noise. The advantages of our method are its simplicity, extremely fast calculation, robustness, and invariance with respect to nonlinear monotonous transformations.
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                Author and article information

                Journal
                PLEEE8
                Physical Review E
                Phys. Rev. E
                American Physical Society (APS)
                2470-0045
                2470-0053
                February 2018
                February 27 2018
                : 97
                : 2
                Article
                10.1103/PhysRevE.97.022223
                80c842ff-49dd-4343-b8f2-d2307d565a4f
                © 2018

                https://link.aps.org/licenses/aps-default-license

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