About this Research Topic

Manuscript Submission Deadline 18 May 2022
Manuscript Extension Submission Deadline 28 October 2022

The fundamental importance of webs/networks in our daily lives has become increasingly evident over the past couple of decades, to the point where the scientific community is actively pursuing the development of a Network Science (NS), with applications in medical, social, and physical sciences. The spectra of application within the various disciplines make clear that there are features of complex webs that supersede specific mechanism and need to be understood from a general perspective. One such perspective is that of 1/f noise for characterizing the variability in the underlying dynamics, inverse power law distributions for the statistics of time intervals between events and the relation between the two.

There are literally hundreds of complex phenomena whose statistical properties are described by inverse power laws and whose degree of complexity is determined by the inverse power law index. These phenomena do not produce arcane events that we encounter only fleetingly but are the incidents that dominate our lives, as well as life itself. The truly unpredictable nature of a bridge collapse, the bursting of an economic bubble, or the onset of a heart attack, each is part of a different elaborate web. The reasons for the failure of normal statistics to predict these things has its roots in the complex dynamic character of networks of physics, physiology, finance, geopolitics, transportation, and on and on. This web-complexity is manifest in time series that have divergent first and second moments, are non-stationary, are non-ergodic, and are non-Poisson. How this new perspective involving networks influences fields of investigation, such as decision making, control theory, adaptation, and information exchange between complex networks provides context for introducing many of the formal scientific ideas necessary for understanding complex phenomena in general.

Measuring and analyzing a network’s various adaptive mechanisms may guide us in understanding how to mitigate the complex dynamics of a network’s malfunction due to disease such as the neurological pathologies of Parkinson's disease, or the physiologic intricacies of mathematical oncology. These ideas may also bridge the gap between medicine and sociology through the manner of information transport in a pandemic and perhaps shed light on how to avoid the Draconian either /or choice of war or peace as the only way to frame the resolution of global disagreements.

Complex phenomena can be modeled as an intricate network-of-networks (NoN), such as that making up the physiology of the human body in the new discipline of network medicine. An exemplar is the neural networks carrying information from one complex physiological subnetwork to another and which coordinate between subnetworks to accomplish a difficult task collectively. For each subnetwork, the remainder of the NoN acts as a complex dynamic environment, and in order for the NoN as a whole to optimally preform its function the subnetworks must continuously adapt to the changes in the host network.

We invite authors to publish their research (experimental, modeling, or theoretical) in adaptation among the subnetworks in all manner of NoN:

• Medical NoNs such as different subnetworks of the brain, heart-brain, etc.

• Natural NoNs such as different subnetworks of the society, econophysics, etc.

• Artificial NoNs such as different AI subnetworks of a self-driving car, UAV, etc.

• Natural-artificial NoN hybrids such as human-machine interfaces, brain-to-brain interfaces, etc.

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.

The fundamental importance of webs/networks in our daily lives has become increasingly evident over the past couple of decades, to the point where the scientific community is actively pursuing the development of a Network Science (NS), with applications in medical, social, and physical sciences. The spectra of application within the various disciplines make clear that there are features of complex webs that supersede specific mechanism and need to be understood from a general perspective. One such perspective is that of 1/f noise for characterizing the variability in the underlying dynamics, inverse power law distributions for the statistics of time intervals between events and the relation between the two.

There are literally hundreds of complex phenomena whose statistical properties are described by inverse power laws and whose degree of complexity is determined by the inverse power law index. These phenomena do not produce arcane events that we encounter only fleetingly but are the incidents that dominate our lives, as well as life itself. The truly unpredictable nature of a bridge collapse, the bursting of an economic bubble, or the onset of a heart attack, each is part of a different elaborate web. The reasons for the failure of normal statistics to predict these things has its roots in the complex dynamic character of networks of physics, physiology, finance, geopolitics, transportation, and on and on. This web-complexity is manifest in time series that have divergent first and second moments, are non-stationary, are non-ergodic, and are non-Poisson. How this new perspective involving networks influences fields of investigation, such as decision making, control theory, adaptation, and information exchange between complex networks provides context for introducing many of the formal scientific ideas necessary for understanding complex phenomena in general.

Measuring and analyzing a network’s various adaptive mechanisms may guide us in understanding how to mitigate the complex dynamics of a network’s malfunction due to disease such as the neurological pathologies of Parkinson's disease, or the physiologic intricacies of mathematical oncology. These ideas may also bridge the gap between medicine and sociology through the manner of information transport in a pandemic and perhaps shed light on how to avoid the Draconian either /or choice of war or peace as the only way to frame the resolution of global disagreements.

Complex phenomena can be modeled as an intricate network-of-networks (NoN), such as that making up the physiology of the human body in the new discipline of network medicine. An exemplar is the neural networks carrying information from one complex physiological subnetwork to another and which coordinate between subnetworks to accomplish a difficult task collectively. For each subnetwork, the remainder of the NoN acts as a complex dynamic environment, and in order for the NoN as a whole to optimally preform its function the subnetworks must continuously adapt to the changes in the host network.

We invite authors to publish their research (experimental, modeling, or theoretical) in adaptation among the subnetworks in all manner of NoN:

• Medical NoNs such as different subnetworks of the brain, heart-brain, etc.

• Natural NoNs such as different subnetworks of the society, econophysics, etc.

• Artificial NoNs such as different AI subnetworks of a self-driving car, UAV, etc.

• Natural-artificial NoN hybrids such as human-machine interfaces, brain-to-brain interfaces, etc.

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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