Spontaneous Formation of Dynamical Groups in an Adaptive Networked System

In this work, we investigate a model of an adaptive networked dynamical system, where the coupling strengths among phase oscillators coevolve with the phase states. It is shown that in this model the oscillators can spontaneously differentiate into two dynamical groups after a long time evolution. Within each group, the oscillators have similar phases, while oscillators in different groups have approximately opposite phases. The network gradually converts from the initial random structure with a uniform distribution of connection strengths into a modular structure which is characterized by strong intra connections and weak inter connections. Furthermore, the connection strengths follow a power law distribution, which is a natural consequence of the coevolution of the network and the dynamics. Interestingly, it is found that if the inter connections are weaker than a certain threshold, the two dynamical groups will almost decouple and evolve independently. These results are helpful in further understanding the empirical observations in many social and biological networks.

💡 Research Summary

The paper introduces an adaptive network model in which the coupling strengths among phase oscillators co‑evolve with the oscillators’ phases. Each oscillator i follows a Kuramoto‑type equation dθ_i/dt = ω_i + Σ_j K_{ij} sin(θ_j−θ_i), while the weight K_{ij} evolves according to a feedback rule that strengthens links between oscillators with similar phases and weakens links between those with opposite phases. Specifically, dK_{ij}/dt = ε·