Research Topic

Current trends on analysis, control, and synchronization of complex networks: from static to dynamic interconnections

About this Research Topic

Complex networks pervade the natural world and are at the heart of many man-made systems. For instance, the insulin in the human body is essentially produced by islets of pancreatic beta cells, brain activity is regulated by large ensembles of neuronal cells, the generation and distribution of electricity is achieved by power grids, and groups of coordinated robotic systems are at the core of advanced manufacturing processes, among others. Also, a very familiar example of a complex network is social media which is composed of virtual communities where people can share ideas, and which nowadays have a large influence on the human social behavior. The complexity in a network may arise from the intrinsic dynamics of the individual nodes, from the interconnections or coupling structure, and/or from a combination of both. Independently of the source of complexity, a complex network may exhibit very interesting dynamic behavior like for example, self-organization (synchronization), emergent behavior, bifurcation scenarios, pattern formation, and chimeras, characterized by the co-existence of synchronized and desynchronized states, among others.

This Research Topic aims to provide a forum for presenting state-of-the-art theoretical, numerical, and experimental results related to the analysis, (self)synchronization, and control of complex networks from a Dynamics and Control perspective. In particular, from a Dynamics viewpoint, the expected contributions should provide new understanding about the underlying mechanisms—related to network topology, intrinsic dynamics, type of interconnections—which are key for the onset of synchronization, emergent behavior, chimera states, among other interesting nonlinear phenomena occurring in complex networks. This includes, but is not limited to, networks of chaotic oscillators, nonminimum phase systems, and biologically inspired systems, among others. In contrast, from a Control Theory perspective, the required contributions should provide novel—robust and energetically efficient—control strategies for artificially inducing certain desired (collective) behavior in complex networks.

A particular subject to be addressed in this Research Topic is the use of dynamic couplings. It is well-known that the classical static couplings, also referred to as diffusive couplings, present certain limitations: for certain networks, (e.g., those composed by chaotic oscillators or non-minimum phase systems) the interval of coupling strength values for which the network exhibits synchronized/coordinated motion shrinks as long as the number of nodes in the network are increased, or even worse, at some point, synchronization is lost when the number of nodes in the network is relatively large. Recent work on this has demonstrated that such limitations on the synchronizability properties of the networks can be reduced or even eliminated if the static interconnections are replaced by suitably designed dynamic couplings. Hence, one of the main purposes of this Research Topic is to motivate further research on this area, i.e., to motivate a transition from static to dynamic interconnections in the study of complex networks.


Keywords: control, synchronization, dynamics, complex system, networks


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Complex networks pervade the natural world and are at the heart of many man-made systems. For instance, the insulin in the human body is essentially produced by islets of pancreatic beta cells, brain activity is regulated by large ensembles of neuronal cells, the generation and distribution of electricity is achieved by power grids, and groups of coordinated robotic systems are at the core of advanced manufacturing processes, among others. Also, a very familiar example of a complex network is social media which is composed of virtual communities where people can share ideas, and which nowadays have a large influence on the human social behavior. The complexity in a network may arise from the intrinsic dynamics of the individual nodes, from the interconnections or coupling structure, and/or from a combination of both. Independently of the source of complexity, a complex network may exhibit very interesting dynamic behavior like for example, self-organization (synchronization), emergent behavior, bifurcation scenarios, pattern formation, and chimeras, characterized by the co-existence of synchronized and desynchronized states, among others.

This Research Topic aims to provide a forum for presenting state-of-the-art theoretical, numerical, and experimental results related to the analysis, (self)synchronization, and control of complex networks from a Dynamics and Control perspective. In particular, from a Dynamics viewpoint, the expected contributions should provide new understanding about the underlying mechanisms—related to network topology, intrinsic dynamics, type of interconnections—which are key for the onset of synchronization, emergent behavior, chimera states, among other interesting nonlinear phenomena occurring in complex networks. This includes, but is not limited to, networks of chaotic oscillators, nonminimum phase systems, and biologically inspired systems, among others. In contrast, from a Control Theory perspective, the required contributions should provide novel—robust and energetically efficient—control strategies for artificially inducing certain desired (collective) behavior in complex networks.

A particular subject to be addressed in this Research Topic is the use of dynamic couplings. It is well-known that the classical static couplings, also referred to as diffusive couplings, present certain limitations: for certain networks, (e.g., those composed by chaotic oscillators or non-minimum phase systems) the interval of coupling strength values for which the network exhibits synchronized/coordinated motion shrinks as long as the number of nodes in the network are increased, or even worse, at some point, synchronization is lost when the number of nodes in the network is relatively large. Recent work on this has demonstrated that such limitations on the synchronizability properties of the networks can be reduced or even eliminated if the static interconnections are replaced by suitably designed dynamic couplings. Hence, one of the main purposes of this Research Topic is to motivate further research on this area, i.e., to motivate a transition from static to dynamic interconnections in the study of complex networks.


Keywords: control, synchronization, dynamics, complex system, networks


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

04 December 2021 Abstract
02 February 2022 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

04 December 2021 Abstract
02 February 2022 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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