The scaling properties of dynamical fluctuations in temporal networks

TL;DR

This paper investigates the scaling properties of dynamical fluctuations in temporal networks using factorial moments analysis, revealing self-similarity and critical behavior similar to phase transitions in physical systems.

Contribution

It introduces a novel analysis of temporal network fluctuations, demonstrating their self-similar and correlated nature through empirical factorial moments analysis.

Findings

Intermittent behaviors observed in fluctuations across all moment orders

Fluctuations exhibit self-similarity in time intervals

Scaling exponents are close to those of the 2D Ising model undergoing a phase transition

Abstract

The factorial moments analyses are performed to study the scaling properties of the dynamical fluctuations of contacts and nodes in temporal networks based on empirical data sets. The intermittent behaviors are observed in the fluctuations for all orders of the moments. It indicates that the interaction has self-similarity structure in time interval and the fluctuations are not purely random but dynamical and correlated. The scaling exponents for contacts in Prostitution data and nodes in Conference data are very close to that for 2D Ising model undergoing a second-order phase transition.