Soumen Majhi, Biswambhar Rakshit, Amit Sharma, Jürgen Kurths, Dibakar Ghosh
{"title":"Dynamical robustness of network of oscillators","authors":"Soumen Majhi, Biswambhar Rakshit, Amit Sharma, Jürgen Kurths, Dibakar Ghosh","doi":"arxiv-2407.02260","DOIUrl":null,"url":null,"abstract":"Most complex systems are nonlinear, relying on emergent behavior from\ninteracting subsystems, often characterized by oscillatory dynamics. Collective\noscillatory behavior is essential for the proper functioning of many real world\nsystems. Complex networks have proven efficient in elucidating the topological\nstructures of both natural and artificial systems and describing diverse\nprocesses occurring within them. Recent advancements have significantly\nenhanced our understanding of emergent dynamics in complex networks. Among\nvarious processes, a substantial body of work explores the dynamical robustness\nof complex networks, their ability to withstand degradation in network\nconstituents while maintaining collective oscillatory dynamics. Many physical\nand biological systems experience a decline in dynamic activities due to\nnatural or environmental factors. The impact of such damages on network\nperformance can be significant, and the system's robustness indicates its\ncapability to maintain functionality despite dynamic changes, often termed\naging. This review provides a comprehensive overview of notable research\nexamining how networks sustain global oscillation despite increasing inactive\ndynamical units. We present contemporary research dedicated to the theoretical\nunderstanding and enhancement mechanisms of dynamical robustness in complex\nnetworks. Our focus includes various network structures and coupling functions,\nelucidating the persistence of networked systems. We cover system\ncharacteristics from heterogeneity in network connectivity to heterogeneity in\ndynamical units. Finally, we discuss challenges in this field and open areas\nfor future studies.","PeriodicalId":501305,"journal":{"name":"arXiv - PHYS - Adaptation and Self-Organizing Systems","volume":"158 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - PHYS - Adaptation and Self-Organizing Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2407.02260","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Most complex systems are nonlinear, relying on emergent behavior from
interacting subsystems, often characterized by oscillatory dynamics. Collective
oscillatory behavior is essential for the proper functioning of many real world
systems. Complex networks have proven efficient in elucidating the topological
structures of both natural and artificial systems and describing diverse
processes occurring within them. Recent advancements have significantly
enhanced our understanding of emergent dynamics in complex networks. Among
various processes, a substantial body of work explores the dynamical robustness
of complex networks, their ability to withstand degradation in network
constituents while maintaining collective oscillatory dynamics. Many physical
and biological systems experience a decline in dynamic activities due to
natural or environmental factors. The impact of such damages on network
performance can be significant, and the system's robustness indicates its
capability to maintain functionality despite dynamic changes, often termed
aging. This review provides a comprehensive overview of notable research
examining how networks sustain global oscillation despite increasing inactive
dynamical units. We present contemporary research dedicated to the theoretical
understanding and enhancement mechanisms of dynamical robustness in complex
networks. Our focus includes various network structures and coupling functions,
elucidating the persistence of networked systems. We cover system
characteristics from heterogeneity in network connectivity to heterogeneity in
dynamical units. Finally, we discuss challenges in this field and open areas
for future studies.