Modules identification by a Dynamical Clustering algorithm based on chaotic R\"ossler oscillators

TL;DR

This paper introduces a modified dynamical clustering algorithm using chaotic R"ossler oscillators to identify modules in complex networks, demonstrating its effectiveness on both real and synthetic networks with known modular structures.

Contribution

The paper presents an improved clustering algorithm based on chaotic R"ossler oscillators, enhancing module detection in complex networks.

Findings

Effective identification of modules in real networks

Robustness of the algorithm on synthetic networks

Enhanced sensitivity compared to previous methods

Abstract

A new dynamical clustering algorithm for the identification of modules in complex networks has been recently introduced \cite{BILPR}. In this paper we present a modified version of this algorithm based on a system of chaotic Roessler oscillators and we test its sensitivity on real and computer generated networks with a well known modular structure.