2025/12/22 by H.O. Wang, Wang, Haoshuai, Zhaoqi Dong +3
Computer Science · #Adaptation and Self-Organizing Systems (nlin.AO) #Distributed Control Multi-Agent Systems #FOS: Physical sciences #Neural Networks Stability and Synchronization #Nonlinear Dynamics and Pattern Formation #Physics and Society (physics.soc-ph)
paper · doi:10.48550/arxiv.2512.19030
openalex publication_date 2025/12/22 · openalex created_date 2025/12/24 · openalex updated_date 2026/07/28
Collective motion is a fundamental phenomenon in biological swarms. As a framework for studying synchronization in motions, the Vicsek model is simple and efficient, assuming isotropic interactions with a complete field of view. Drawing inspiration from natural swarms, we incorporate realistic constraints into the model. By analysing the interaction structures from the complex network perspective, we demonstrate that models with the homogeneous interaction rules naturally form Erdos-Renyi networks, whereas the introduction of heterogeneity leads to Barabasi-Albert networks. Furthermore, we discover that the model's synchronization is fundamentally governed by the average degree of the interaction network. Through a comparative analysis across these topologies, we identify a stretched-exponential relationship between the average degree and the synchronization metrics.