Identification and Analysis of Dynamic Structures in Complex Systems: Algorithms and Applications

Dear Colleagues,

Identifying emergent structures in dynamical systems composed of many interacting parts is a major challenge in science. In particular, the formation of intermediate-level dynamical structures is of particular interest in biological and artificial systems. These structures emerge from the dynamics of small-scale processes. However, they possess some peculiar characteristics distinctive enough to make it worth giving them a specific name – e.g., vortexes in fluid dynamics, autocatalytic systems in chemistry, organs in organisms, species in ecosystems or parties, and coalitions in socio-technological systems. Once these structures are determined, they have an undeniable influence on both the system they belong to and the entities that form them. These structures can also greatly influence the processing of information by the system as a whole. Identifying these configurations is seldom simple because of the more-than-binary relationships among variables, the multiple memberships of the system entities, and the fuzziness of boundaries among groups. Scientists are currently searching for algorithms and tools to define, detect, and identify these dynamically relevant structures in many fields: their recognition would constitute an essential step toward understanding the systems’ structure.

From a technological viewpoint, this problem is often tackled by methods that rely on principles from information theory and data mining tools. The description of complex systems is naturally related to the concept of big data – when not for their actual dimensions, for the high number of variables that are usually needed to describe their dynamics – and with data mining. Therefore, a fascinating, yet little explored, opportunity offered by this study can also be represented by “reverting the process” and using complex system analysis principles to derive widely applicable data analysis algorithms.

This Special Issue aims to foster discussion on the dynamics of complex systems from an algorithmic viewpoint, emphasizing the detection of relevant integrated structures or subsystems and the understanding of their relationship with the dynamics of the whole system (and its environment). The Special Issue welcomes papers that highlight relationships between algorithms applicable to such problems and methods, derived from them or being their extensions, that can be applied more widely to other fields, such as data analysis, machine learning, and pattern recognition.

Dr. Marco Villani
Prof. Dr. Stefano Cagnoni
Guest Editors

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• Detecting integrated dynamical structures 
• Statistical approaches to complex system analysis 
• Information theory methods for studying the dynamics of complex systems 
• Studying the emergence and dynamics of meso-level structures 
• Studying the emergence of boundaries, sensors, and actuators 
• Discovering, designing, and driving hidden dynamical structures 
• Machine learning methods for studying the dynamics of complex systems 
• Relationships between big data and complex systems analysis 
• Data analysis and machine learning methods drawing inspiration from complex system analysis