Information propagation in multilayer systems with higher-order interactions across timescales

Abstract

Complex systems are characterized by multiple spatial and temporal scales. A natural framework to capture their multiscale nature is that of multilayer networks, where different layers represent distinct physical processes that often regulate each other indirectly. We model these regulatory mechanisms through triadic higher-order interactions between nodes and edges. In this work, we focus on how the different timescales associated with each layer impact their effective couplings in terms of their mutual information. We unravel the general principles governing how such information propagates across the multiscale structure, and apply them to study archetypal examples of biological signaling networks and effective environmental dependencies in stochastic processes. Our framework generalizes to any dynamics on multilayer networks, paving the way for a deeper understanding of how the multiscale nature of real-world systems shapes their information content and complexity.

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• Capturing long-range memory structures with tree-geometry process tensors
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• Triadic percolation on multilayer networks
• Finite size scaling of survival statistics in metapopulation models
• Unravelling the Flow of Information in a Nonequilibrium Process in the Presence of Hydrodynamic Interactions
• Generalized Derangetropy Functionals for Modeling Cyclical Information Flow