Year: 2018
Type: article
Abstract: Stimulated by the growing interest in the applications of complex networks framework for time series analysis, we devise a network model in which each of $N$ nodes is associated with a random walk of length $L$. Connectivity between any two nodes is established when the Pearson correlation coefficient (PCC) between the corresponding time series is greater than or equal to a threshold $H$, resulting in similarity networks with interesting properties. In particular, these networks can have high average clustering coefficients and the ``small world'' property, and their degree distribution can vary from scale-free to quasiconstant depending on $H$. A giant component of size $N$ exists until a critical threshold ${H}_{c}$ is crossed, at which point relatively rare walks begin to detach from it and remain isolated. This model can be used as a first step for building a null hypothesis for networks constructed from time series.
Source: Physical review. E
Authors: Harinder Pal, Thomas H. Seligman, Juan V. Escobar
Institution: Universidad Nacional Autónoma de México
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2806007069