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Delayed random walk on deterministic weighted scale-free small-world network with a deep trap
Modern Physics Letters B ( IF 1.8 ) Pub Date : 2020-07-29 , DOI: 10.1142/s0217984920503339
Guangyao Xu 1 , Zikai Wu 1
Affiliation  

How to effectively control the trapping process in complex systems is of great importance in the study of trapping problem. Recently, the approach of delayed random walk has been introduced into several deterministic network models to steer trapping process. However, exploring delayed random walk on pseudo-fractal web with the co-evolution of topology and weight has remained out of reach. In this paper, we employ delayed random walk to guide trapping process on a salient deterministic weighted scale-free small-world network with the co-evolution of topology and weight. In greater detail, we first place a deep trap at one of initial nodes of the network. Then, a tunable parameter [Formula: see text] is introduced to modulate the transition probability of random walk and dominate the trapping process. Subsequently, trapping efficiency is used as readout of trapping process and average trapping time is employed to measure trapping efficiency. Finally, the closed form solution of average trapping time (ATT) is deduced analytically, which agrees with corresponding numerical solution. The analytical solution of ATT shows that the delayed parameter [Formula: see text] only modifies the prefactor of ATT, and keeps the leading scaling unchanged. In other words, ATT grows sublinearly with network size, whatever values [Formula: see text] takes. In summary, the work may serves as one piece of clues for modulating trapping process toward desired efficiency on more general deterministic networks.

中文翻译:

具有深度陷阱的确定性加权无标度小世界网络上的延迟随机游走

如何有效控制复杂系统中的诱捕过程对于诱捕问题的研究具有重要意义。最近,延迟随机游走的方法已被引入几个确定性网络模型来引导捕获过程。然而,探索伪分形网络上的延迟随机游走与拓扑和权重的共同演化仍然遥不可及。在本文中,我们采用延迟随机游走来指导具有拓扑和权重共同演化的显着确定性加权无标度小世界网络上的捕获过程。更详细地说,我们首先在网络的一个初始节点处放置一个深度陷阱。然后,引入一个可调参数 [公式:见正文] 来调节随机游走的转移概率并主导捕获过程。随后,捕集效率用作捕集过程的读数,并采用平均捕集时间来衡量捕集效率。最后,解析推导出平均捕获时间(ATT)的闭式解,与相应的数值解相吻合。ATT的解析解表明,延迟参数[公式:见正文]只修改了ATT的前置因子,保持领先的缩放比例不变。换句话说,无论 [公式:见文本] 采用什么值,ATT 都随着网络规模呈亚线性增长。总之,这项工作可以作为一条线索,用于在更一般的确定性网络上调整捕获过程以达到所需的效率。解析推导了平均俘获时间(ATT)的闭式解,与相应的数值解相吻合。ATT的解析解表明,延迟参数[公式:见正文]只修改了ATT的前置因子,保持领先的缩放比例不变。换句话说,无论 [公式:见文本] 采用什么值,ATT 都随着网络规模呈亚线性增长。总之,这项工作可以作为一条线索,用于在更一般的确定性网络上调整捕获过程以达到所需的效率。解析推导了平均俘获时间(ATT)的闭式解,与相应的数值解相吻合。ATT的解析解表明,延迟参数[公式:见正文]只修改了ATT的前置因子,保持领先的缩放比例不变。换句话说,无论 [公式:见文本] 采用什么值,ATT 都随着网络规模呈亚线性增长。总之,这项工作可以作为一条线索,用于在更一般的确定性网络上调整捕获过程以达到所需的效率。见正文]需要。总之,这项工作可以作为一条线索,用于在更一般的确定性网络上调整捕获过程以达到所需的效率。见正文]需要。总之,这项工作可以作为一条线索,用于在更一般的确定性网络上调整捕获过程以达到所需的效率。
更新日期:2020-07-29
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