TL;DR
This paper introduces an efficient dynamic cavity method for sparse Boolean networks, enabling analysis of non-equilibrium steady states and correlations, especially in networks with complex degree distributions and bidirectional interactions.
Contribution
It develops a computationally efficient implementation of the dynamic cavity method for Boolean networks, allowing exploration of non-equilibrium properties and motif-induced correlations.
Findings
Two basic motifs can describe observed correlations.
Networks with symmetric interactions favor active states.
Networks with anti-symmetric interactions favor inactive states.
Abstract
Dynamic processes of interacting units on a network are out of equilibrium in general. In the case of a directed tree, the dynamic cavity method provides an efficient tool that characterises the dynamic trajectory of the process for the linear threshold model. However, because of the computational complexity of the method, the analysis has been limited to systems where the largest number of neighbours is small. We devise an efficient implementation of the dynamic cavity method which substantially reduces the computational complexity of the method for systems with discrete couplings. Our approach opens up the possibility to investigate the dynamic properties of networks with fat-tailed degree distribution. We exploit this new implementation to study properties of the non-equilibrium steady-state. We extend the dynamical cavity approach to calculate the pairwise correlations induced by…
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