具有依赖距离的相互作用的二维加性小世界网络

R. A. Dumer, M. Godoy
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引用次数: 0

摘要

在这项研究中,我们采用蒙特卡洛计算方法,研究了二维加性小世界网络上的伊辛模型,该网络的长程相互作用取决于相互作用位点之间的几何距离。该网络最初由一个规则的正方形晶格定义,以概率 $p$ 对每个站点进行测试,看其是否有可能与其他尚未接收到长程相互作用的站点产生长程相互作用。在这里,我们使用了 $p=1$的特殊情况,即网络中的每个位点除了与规则方格的短程相互作用外,还有一个长程相互作用。这些长程相互作用取决于幂律形式,即 $J_{ij}=r_{ij}^{-\alpha}$,与相连位点 $i$ 和 $j$ 之间的几何距离 $r_{ij}$。在当前的二维模型中,我们发现只有在 $\alpha=0$ 时才能观察到平均场临界行为。随着 $\alpha$ 的增大,网络大小会影响系统的相变点,即显示出交叉行为。然而,在二维系统中,我们发现短程相互作用的临界行为在 $\alpha\approx2$ 时。因此,与全局耦合模型相比,长程相互作用的数量限制以及这些相互作用的衰减形式,使我们无法找到一个具有有限相变点和连续变化的临界指数在 $0本文章由计算机程序翻译,如有差异,请以英文原文为准。
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2D additive small-world network with distance-dependent interactions
In this work, we have employed Monte Carlo calculations to study the Ising model on a 2D additive small-world network with long-range interactions depending on the geometric distance between interacting sites. The network is initially defined by a regular square lattice and with probability $p$ each site is tested for the possibility of creating a long-range interaction with any other site that has not yet received one. Here, we used the specific case where $p=1$, meaning that every site in the network has one long-range interaction in addition to the short-range interactions of the regular lattice. These long-range interactions depend on a power-law form, $J_{ij}=r_{ij}^{-\alpha}$, with the geometric distance $r_{ij}$ between connected sites $i$ and $j$. In current two-dimensional model, we found that mean-field critical behavior is observed only at $\alpha=0$. As $\alpha$ increases, the network size influences the phase transition point of the system, i.e., indicating a crossover behavior. However, given the two-dimensional system, we found the critical behavior of the short-range interaction at $\alpha\approx2$. Thus, the limitation in the number of long-range interactions compared to the globally coupled model, as well as the form of the decay of these interactions, prevented us from finding a regime with finite phase transition points and continuously varying critical exponents in $0<\alpha<2$.
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arXiv - PHYS - Statistical Mechanics
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