Robustness and Resilience of Complex Networks

Dear Colleagues,

This Special Issue promotes interdisciplinary research and looks into the topic of robustness and resilience for complex systems and networks. This translational topic, which is strongly emerging in pure and applied research, calls for multifaceted approaches, integrating techniques from network theory, control engineering, information theory, and more, as well as applied research from single disciplines like ecology, physics, engineering, management, and so forth.

Natural and artificial systems and networks often share the capability to maintain critical functions and properties despite uncertainties, fluctuations, and perturbations, both in their topology and in their dynamics. Multidisciplinary endeavors are dedicated to unraveling the key characteristics, such as structural, mechanical, and dynamical, that guarantee such behavior, to developing comprehensive frameworks to study it, and to detecting and anticipating losses of robustness and resilience. Additional research avenues in the direction of management and control are also warranted.

We thus ask for contributions around this thrilling topic, both theoretical and applied, in order to frame a comprehensive picture of the quantitative theories and techniques to address the question of dynamical networks persisting in their functions despite alterations.

We thus call for original research papers and comprehensive reviews about, but not limited to, the following topics:

• Investigation of network robustness (in network theory sense) against topological alterations;
• Investigation of system robustness (in control sense) under parametric uncertainties;
• Modeling of system and network resilience, i.e., persistence of function despite uncertainties and perturbations;
• Multidisciplinary contributions on the framework of robustness and resilience, employing methods from mathematics, bifurcation theory, information theory, and more;
• Development and performance assessment of indicators for resilience loss, both model- and data-driven;
• Advances in detection and anticipation of tipping points in complex networks, both model-based and data-driven;
• Cross-fertilization between the fields of system robustness and critical transitions;
• Suggestions for control of fragile and hysteretic systems;
• Advanced computational algorithms in real problems;
• Domain-specific applications, employing models or empirical data.

Dr. Daniele Proverbio
Dr. Stefano Boccaletti
Guest Editors

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