Community structure in real-world networks from a non-parametrical synchronization-based dynamical approach

This work analyzes the problem of community structure in real-world networks based on the synchronization of nonidentical coupled chaotic Rössler oscillators each one characterized by a defined natural frequency, and coupled according to a predefined network topology. The interaction scheme contemplates an uniformly increasing coupling force to simulate a society in which the association between the agents grows in time. To enhance the stability of the correlated states that could emerge from the synchronization process, we propose a parameterless mechanism that adapts the characteristic frequencies of coupled oscillators according to a dynamic connectivity matrix deduced from correlated data. We show that the characteristic frequency vector that results from the adaptation mechanism reveals the underlying community structure present in the network.

► A synchronization-based algorithm for community structure detection is proposed.
► We model a complex network based on coupled nonidentical chaotic Rössler oscillators.
► The interaction scheme contemplates an uniformly increasing coupling force.
► The frequencies of oscillators are adapted according to a parameterless mechanism.
► The adaptation mechanism reveals the community structure present in the network.