Maximum entropy network states for coalescence processes

Arsham Ghavasieh and Manlio De Domenico

Physica A: Statistical Mechanics and its Applications, 2024, vol. 643, issue C

Abstract: Complex network states are characterized by the interplay between system’s structure and dynamics. One way to represent such states is by means of network density matrices, whose von Neumann entropy characterizes the number of distinct microstates compatible with given topology and dynamical evolution. In this Letter, we propose a maximum entropy principle to characterize network states for systems with heterogeneous, generally correlated, connectivity patterns and non-trivial dynamics. We focus on three distinct coalescence processes, widely encountered in the analysis of empirical interconnected systems, and characterize their entropy and transitions between distinct dynamical regimes across distinct temporal scales. Our framework allows one to study the statistical physics of systems that aggregate, such as in transportation infrastructures serving the same geographic area, or correlate, such as inter-brain synchrony arising in organisms that socially interact, and active matter that swarm or synchronize.

Keywords: Network science; Complex networks; Network information theory; Network density matrix; Network dynamics; Network coalescence (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:643:y:2024:i:c:s0378437124002619

DOI: 10.1016/j.physa.2024.129752

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