Fractal Analysis on Human Behaviors Dynamics

TL;DR

This paper investigates the fractal nature of human behavior patterns through time series analysis of library loan data, revealing long-range correlations and complex network structures that reflect intrinsic regularities in collective human actions.

Contribution

It introduces a novel approach combining fractal analysis and complex network theory to study human behavior dynamics from library loan data.

Findings

Human behavior time series exhibit fractal properties with long-range correlations.

Complex networks derived from behaviors show scale-free, small-world, and hierarchical features.

Networks are not fractal, indicating complex but non-self-similar structures.

Abstract

The study of human dynamics has attracted much interest from many fields recently. In this paper, the fractal characteristic of human behaviors is investigated from the perspective of time series constructed with the amount of library loans. The Hurst exponents and length of non-periodic cycles calculated through Rescaled Range Analysis indicate that the time series of human behaviors is fractal with long-range correlation. Then the time series are converted to complex networks by visibility graph algorithm. The topological properties of the networks, such as scale-free property, small-world effect and hierarchical structure imply that close relationships exist between the amounts of repetitious actions performed by people during certain periods of time, especially for some important days. Finally, the networks obtained are verified to be not fractal and self-similar using box-counting method. Our work implies the intrinsic regularity shown in human collective repetitious behaviors.