Corder Rodrigo M, Bian Zheng, Pereira Tiago, Montalbán Antonio
Division of Epidemiology and Biostatistics, School of Public Health, University of California, Berkeley, Berkeley, California 94720, USA.
Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, São Carlos 13566-590, Brazil.
Chaos. 2023 Sep 1;33(9). doi: 10.1063/5.0169628.
Many real-world complex systems rely on cluster synchronization to function properly. A cluster of nodes exhibits synchronous behavior, while others behave erratically. Predicting the emergence of these clusters and understanding the mechanism behind their structure and variation in response to a parameter change is a daunting task in networks that lack symmetry. We unravel the mechanism for the emergence of cluster synchronization in heterogeneous random networks. We develop heterogeneous mean-field approximation together with a self-consistent theory to determine the onset and stability of the cluster. Our analysis shows that cluster synchronization occurs in a wide variety of heterogeneous networks, node dynamics, and coupling functions. The results could lead to a new understanding of the dynamical behavior of networks ranging from neural to social.
许多现实世界中的复杂系统依靠集群同步来正常运行。一组节点表现出同步行为,而其他节点则行为不稳定。在缺乏对称性的网络中,预测这些集群的出现并理解其结构背后的机制以及响应参数变化时的变化情况是一项艰巨的任务。我们揭示了异构随机网络中集群同步出现的机制。我们开发了异构平均场近似以及自洽理论来确定集群的起始和稳定性。我们的分析表明,集群同步发生在各种各样的异构网络、节点动态和耦合函数中。这些结果可能会引发对从神经到社会等各种网络动态行为的新理解。