Analysis of self-equilibrated networks through cellular modeling

TL;DR

This paper introduces a novel cellular modeling approach to analyze self-equilibrated networks, enabling the study of complex systems' equilibrium states through elementary network units called cells.

Contribution

It proposes a new method modeling self-equilibrated networks as collections of cells, simplifying the analysis of their equilibrium states using graph theory and geometrical considerations.

Findings

The cellular approach effectively models complex self-equilibrated networks.

The method allows analysis of equilibrium states through individual cell interactions.

Examples demonstrate the approach's flexibility and applicability.

Abstract

Network equilibrium models represent a versatile tool for the analysis of interconnected objects and their relationships. They have been widely employed in both science and engineering to study the behavior of complex systems under various conditions, including external perturbations and damage. In this paper, network equilibrium models are revisited through graph-theory laws and attributes with special focus on systems that can sustain equilibrium in the absence of external perturbations (self-equilibrium). A new approach for the analysis of self-equilibrated networks is proposed; they are modeled as a collection of cells, predefined elementary network units that have been mathematically shown to compose any self-equilibrated network. Consequently, the equilibrium state of complex self-equilibrated systems can be obtained through the study of individual cell equilibria and their interactions. A series of examples that highlight the flexibility of network equilibrium models are included in the paper. The examples attest how the proposed approach, which combines topological as well as geometrical considerations, can be used to decipher the state of complex systems.