# Recurrence Network Measures for the Logistic Map

Recurrence Network Measures for the Logistic Map

The degree centrality and clustering coefficient of nodes in the recurrence network of a time series reveal complementary geometrical properties of the dynamics in phase space. This helps to distinguish between the various dynamical regimes of a complex nonlinear system.

The logistic map provides a good model for dynamical transitions among regular, laminar, and chaotic behavior of a dynamical system. The evolution and properties of the time series depend on the control parameter .

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Observe how the degree and clustering series and frequency distribution evolve as you change the control parameter . Can you identify periodic windows in a sea of chaos? As you increase the recurrence threshold , the recurrence network measures slowly approach those expected for a fully connected network.

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