Ising model in clustered scale-free networks

TL;DR

This study investigates the phase transition behavior of the Ising model on clustered scale-free networks, revealing how clustering and degree distribution influence the ferromagnetic-paramagnetic transition temperature.

Contribution

It provides new insights into how clustering motifs like triangles affect phase transitions in scale-free networks with different degree exponents.

Findings

For gamma > 3, a finite critical temperature T_c exists in the thermodynamic limit.

For gamma <= 3, a size-dependent crossover temperature T_co diverges as system size increases.

Clustering increases the transition temperature, especially for gamma <= 3, by affecting network correlations.

Abstract

The Ising model in clustered scale-free networks has been studied by Monte Carlo simulations. These networks are characterized by a degree distribution of the form P(k) ~ k^(-gamma) for large k. Clustering is introduced in the networks by inserting triangles, i.e., triads of connected nodes. The transition from a ferromagnetic (FM) to a paramagnetic (PM) phase has been studied as a function of the exponent gamma and the triangle density. For gamma > 3 our results are in line with earlier simulations, and a phase transition appears at a temperature T_c(gamma) in the thermodynamic limit (system size N to infinity). For gamma <= 3, a FM-PM crossover appears at a size-dependent temperature T_co, so that the system remains in a FM state at any finite temperature in the limit N to infinity. Thus, for gamma = 3, T_co scales as ln N, whereas for gamma < 3, we find T_co ~ J N^z, where the exponent z decreases for increasing gamma. Adding motifs (triangles in our case) to the networks causes an increase in the transition (or crossover) temperature for exponent gamma > 3 (or <= 3). For gamma > 3, this increase is due to changes in the mean values < k > and < k^2 >, i.e., the transition is controlled by the degree distribution (nearest neighbor connectivities). For gamma <= 3, however, we find that clustered and unclustered networks with the same size and distribution P(k) have different crossover temperature, i.e., clustering favors FM correlations, thus increasing the temperature T_co. The effect of a degree cutoff k_cut on the asymptotic behavior of T_co is discussed.