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By constructing a multicanonical Monte Carlo simulation, we obtain the full probability distribution $rho_N(r)$ of the degree assortativity coefficient $r$ on configuration networks of size $N$ by using the multiple histogram reweighting method. We suggest that $rho_N(r)$ obeys a large deviation principle, $rho_N left( r- r_N^* right) asymp {e^{ - {N^xi }Ileft( {r- r_N^* } right)}}$, where the rate function $I$ is convex and possesses its unique minimum at $r=r_N^*$, and $xi$ is an exponent that scales $rho_N$s with $N$. We show that $xi=1$ for Poisson random graphs, and $xigeq1$ for scale-free networks in which $xi$ is a decreasing function of the degree distribution exponent $gamma$. Our results reveal that the fluctuations of $r$ exhibits an anomalous scaling with $N$ in highly heterogeneous networks.
The following question is the subject of our work: could a two-dimensional random path pushed by some constraints to an improbable large deviation regime, possess extreme statistics with one-dimensional Kardar-Parisi-Zhang (KPZ) fluctuations? The ans
The typical values and fluctuations of time-integrated observables of nonequilibrium processes driven in steady states are known to be characterized by large deviation functions, generalizing the entropy and free energy to nonequilibrium systems. The
Degree assortativity refers to the increased or decreased probability of connecting two neurons based on their in- or out-degrees, relative to what would be expected by chance. We investigate the effects of such assortativity in a network of theta ne
The climate system is a complex, chaotic system with many degrees of freedom and variability on a vast range of temporal and spatial scales. Attaining a deeper level of understanding of its dynamical processes is a scientific challenge of great urgen
Employing the optimal fluctuation method (OFM), we study the large deviation function of long-time averages $(1/T)int_{-T/2}^{T/2} x^n(t) dt$, $n=1,2, dots$, of centered stationary Gaussian processes. These processes are correlated and, in general, n