Possible origin for the similar phase transitions in k-core and interdependent networks

物理 渗透(认知心理学) 指数 统计物理学 芯(光纤) 缩放比例 相似性(几何) 临界指数 维数(图论) 分形 分形维数 组合数学 相变 凝聚态物理 数学 数学分析 图像(数学) 几何学 人工智能 计算机科学 生物 光学 语言学 哲学 神经科学
作者
Shengling Gao,Leyang Xue,Bnaya Gross,Zhikun She,Daqing Li,Shlomo Havlin
出处
期刊:New Journal of Physics [IOP Publishing]
卷期号:26 (1): 013006-013006 被引量:4
标识
DOI:10.1088/1367-2630/ad1539
摘要

Abstract The models of k -core percolation and interdependent networks (IN) have been extensively studied in their respective fields. A recent study has revealed that they share several common critical exponents. However, several newly discovered exponents in IN have not been explored in k -core percolation, and the origin of the similarity still remains unclear. Thus, in this paper, by considering k -core percolation on random networks, we first verify that the two newly discovered exponents (fractal fluctuation dimension, d f , and correlation length exponent, ν ) observed in d -dimensional IN spatial networks also exist with the same values in k -core percolation. That is, the fractality of the k -core giant component fluctuations is manifested by a fractal fluctuation dimension, d ˜ f = 3 / 4 , within a correlation size N ʹ that scales as N ( p p c ) ν ˜ , with ν ˜ = 2 . Here we define, ν ˜ d ν and d ˜ f d f / d . This implies that both models, IN and k -core, feature the same scaling behaviors with the same critical exponents, further reinforcing the similarity between the two models. Furthermore, we suggest that these two models are similar since both have two types of interactions: short-range (SR) connectivity links and long-range (LR) influences. In IN the LR are the influences of dependency links while in k -core we find here that for k = 1 and k = 2 the influences are SR and in contrast for k 3 the influence is LR. In addition, analytical arguments for a universal hyper-scaling relation for the fractal fluctuation dimension of the k -core giant component and for IN as well as for any mixed-order transition are established. Our analysis enhances the comprehension of k -core percolation and supports the generalization of the concept of fractal fluctuations in mixed-order phase transitions.
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