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arXiv 2012.10749physics.soc-ph
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Achlioptas过程下生长网络的渗流跃迁:解析解

Percolation Transitions in Growing Networks Under Achlioptas Processes: Analytic Solutions

Soo Min Oh, Seung-Woo Son, Byungnam Kahng

摘要:

网络在各种现实世界系统中无处不在。许多经验网络随着节点数量的增加而增长。增长随机网络中的逾渗跃迁可以是无限阶的。然而,当大团簇的生长在某些效应下被抑制时,例如,Achlioptas过程,过渡类型变为二级。然而,分析结果的临界行为,如转变点,临界指数,和标度关系是罕见的。在这里,我们推导出他们明确作为一个控制参数$m$的功能,表示使用缩放anomaly的抑制强度。然后,我们通过求解速率方程和进行数值模拟证实了结果。我们的结果清楚地表明,过渡点接近1和序参数指数$β$接近零代数为$m \to \infty$,而他们接近这些值的指数为静态网络。此外,上临界维数为$d_u=4$的增长网络,而它是$d_u=2$的静态网络。

英文摘要:

Networks are ubiquitous in diverse real-world systems. Many empirical networks grow as the number of nodes increases with time. Percolation transitions in growing random networks can be of infinite order. However, when the growth of large clusters is suppressed under some effects, e.g., the Achlioptas process, the transition type changes to the second order. However, analytical results for the critical behavior, such as the transition point, critical exponents, and scaling relations are rare. Here, we derived them explicitly as a function of a control parameter $m$ representing the suppression strength using the scaling ansatz. We then confirmed the results by solving the rate equation and performing numerical simulations. Our results clearly show that the transition point approaches unity and the order-parameter exponent $β$ approaches zero algebraically as $m \to \infty$, whereas they approach these values exponentially for a static network. Moreover, the upper critical dimension becomes $d_u=4$ for growing networks, whereas it is $d_u=2$ for static ones.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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