动力系统理论
计算机科学
网络拓扑
同步(交流)
复杂网络
振荡(细胞信号)
复杂系统
分布式计算
网络动力学
拓扑(电路)
集体行为
现象
背景(考古学)
统计物理学
人工智能
物理
数学
电信
频道(广播)
离散数学
量子力学
组合数学
社会学
生物
万维网
人类学
遗传学
操作系统
古生物学
作者
Soumen Majhi,Sarbendu Rakshit,Dibakar Ghosh
出处
期刊:Chaos
[American Institute of Physics]
日期:2022-04-01
卷期号:32 (4)
被引量:4
摘要
Complex network theory has offered a powerful platform for the study of several natural dynamic scenarios, based on the synergy between the interaction topology and the dynamics of its constituents. With research in network theory being developed so fast, it has become extremely necessary to move from simple network topologies to more sophisticated and realistic descriptions of the connectivity patterns. In this context, there is a significant amount of recent works that have emerged with enormous evidence establishing the time-varying nature of the connections among the constituents in a large number of physical, biological, and social systems. The recent review article by Ghosh et al. [Phys. Rep. 949, 1-63 (2022)] demonstrates the significance of the analysis of collective dynamics arising in temporal networks. Specifically, the authors put forward a detailed excerpt of results on the origin and stability of synchronization in time-varying networked systems. However, among the complex collective dynamical behaviors, the study of the phenomenon of oscillation suppression and that of other diverse aspects of synchronization are also considered to be central to our perception of the dynamical processes over networks. Through this review, we discuss the principal findings from the research studies dedicated to the exploration of the two collective states, namely, oscillation suppression and chimera on top of time-varying networks of both static and mobile nodes. We delineate how temporality in interactions can suppress oscillation and induce chimeric patterns in networked dynamical systems, from effective analytical approaches to computational aspects, which is described while addressing these two phenomena. We further sketch promising directions for future research on these emerging collective behaviors in time-varying networks.
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