Graphs and Networks from an Algorithmic Information Perspective

Dear Colleagues,

Graph theory and network science are classic subjects in mathematics widely investigated in the 20th century, transforming research in many fields of science from economy to medicine. However, it has become increasingly clear that the challenge of analyzing these networks cannot be addressed by tools relying solely on statistical methods. Therefore, model-driven approaches are needed. The analysis of such networks is even more challenging for multiscale, multilayer networks, that are neither static nor in an equilibrium state. Recent advances in network science suggest that algorithmic information theory could play an increasingly important role in breaking those limits imposed by traditional statistical analysis in modeling evolving complex networks or interacting networks. Further progress on this front calls for new techniques for an improved mechanistic understanding of complex systems, thereby calling out for increased interaction between network theory and algorithmic information theory.

This Topical Collection is a forum for the presentation and exploration of the foundations of new and improved techniques for the analysis and interpretation of real-world natural and engineered complex systems. Not only from the perspective of algorithmic information theory but also in connection with dynamical systems and causality. Contributions addressing any of these issues and topics are very welcome. Model-driven techniques augmenting explainability or better interpretable approaches to machine learning, better causation-grounded graphical models, and algorithmic information dynamics in application to networks, all fall within the scope of this Topical Collection. However, approaches based on popular statistical compression algorithms such as Lempel-Ziv and its cognates are excluded.

Dr. Narsis A. Kiani
Dr. Hector Zenil
Prof. Dr. Jesper Tegnér
Collection Editors

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