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Fuzzy weighted recurrence networks of time series

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  • Pham, Tuan D.

Abstract

The concept of networks in the context of graph theory delineates a wide variety of real-life complex systems. The theory of networks finds its applications very useful in many scientific and intellectual domains. Weighted networks can characterize complex statistical graph properties, particularly where node connections are heterogeneous. A framework of fuzzy weighted recurrence networks of time series is presented in this letter. Popular graph measures including the average clustering coefficient and characteristic path length of fuzzy weighted recurrence networks are shown to be more robust than those of unweighted recurrence networks derived from binary recurrence plots.

Suggested Citation

  • Pham, Tuan D., 2019. "Fuzzy weighted recurrence networks of time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 409-417.
    • Handle: RePEc:eee:phsmap:v:513:y:2019:i:c:p:409-417
    DOI: 10.1016/j.physa.2018.09.035
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    Cited by:

    1. Pham, Tuan D., 2020. "Fuzzy cross and fuzzy joint recurrence plots," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).

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