A review arguing that generalizing network structure does not escape the fundamental problems of intrinsicality, universality, and functional meaningfulness that already affect standard brain network models.
Realizing interdependent couplings as thermal or higher-order interactions
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abstract
Interdependence is a fundamental ingredient to analyze the stability of many real-world complex systems featuring functional liasons. Yet, physical realizations of this coupling are still unknown, due to the lack of a theoretical framework for their study. To address this gap, we develop an interdependent magnetization framework and show that dependency links between $K-1$ pairwise networks of Ising spins can be rigorously mapped to directed $K$-spin interactions or to adaptive thermal couplings. We adopt the thermal portrait to determine analytically the phase diagram of the model under different structural configurations and we corroborate our results by extensive simulations. We find that interdependence acts like an entropic force that amplifies site-to-site thermal fluctuations, yielding unusual forms of vulnerability and making the system's functioning often unrecoverable. Finally, we discover an isomorphism between the ground state of random multi-spin models and interdependent percolation on randomly coupled networks. This connection raises new perspectives of cross-fertilization, providing unfamiliar methods with relevant implications in the study of constraint satisfaction as well as to the functional robustness of interdependent systems.
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Biological detail and graph structure in network neuroscience
A review arguing that generalizing network structure does not escape the fundamental problems of intrinsicality, universality, and functional meaningfulness that already affect standard brain network models.