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arXiv 1301.0876cond-mat.stat-mech
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具有完美陷阱的随机游动的最优无标度网络

Optimal scale-free network with a minimum scaling of transport efficiency for random walks with a perfect trap

Yihang Yang, Zhongzhi Zhang

摘要:

平均捕获时间(ATT)是捕获问题的核心,因为它是表征问题效率的关键指标。以前的研究提供了一个下界的ATT的缩放一般网络中的随机游动深陷阱。然而,仍然没有很好地理解在哪些网络中可以达到这种最小缩放。特别是,明确的定量结果ATT在这样的网络中,即使在一个特定的网络,是缺乏的,尽管这样的网络揭示了最佳网络的设计具有最高的捕获效率。本文研究了具有完全陷阱的层次无标度网络中的陷阱问题。我们专注于四个代表性的情况下,固定陷阱位于根,外围节点,邻居的根与一个单一的连接,和最远的节点从根,分别。对于所有的四种情况下,我们得到的封闭形式的ATT的公式,以及其领先的标度。我们发现,对于所有四种情况下的捕获问题,ATT的主导标度可以达到预测的最小标度。这一工作加深了对无标度网络中陷阱行为的理解,有助于设计具有最有效传输过程的网络。

英文摘要:

Average trapping time (ATT) is central in the trapping problem since it is a key indicator characterizing the efficiency of the problem. Previous research has provided the scaling of a lower bound of the ATT for random walks in general networks with a deep trap. However, it is still not well understood in which networks this minimal scaling can be reached. Particularly, explicit quantitative results for ATT in such networks, even in a specific network, are lacking, in spite that such networks shed light on the design for optimal networks with the highest trapping efficiency. In this paper, we study the trapping problem taking place on a hierarchical scale-free network with a perfect trap. We focus on four representative cases with the immobile trap located at the root, a peripheral node, a neighbor of the root with a single connectivity, and a farthest node from the root, respectively. For all the four cases, we obtain the closed-form formulas for the ATT, as well as its leading scalings. We show that for all the four cases of trapping problems, the dominating scalings of ATT can reach the predicted minimum scalings. This work deepens the understanding of behavior of trapping in scale-free networks, and is helpful for designing networks with the most efficient transport process.

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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