谢尔宾斯基三角
数学
指数
跳跃
俘获
缩放比例
表达式(计算机科学)
k-最近邻算法
组合数学
随机游动
价值(数学)
统计物理学
分形
数学分析
物理
统计
计算机科学
人工智能
量子力学
几何学
生态学
哲学
语言学
生物
程序设计语言
作者
Yun Chen,Zhizhuo Zhang,Bo Wu
出处
期刊:Fractals
[World Scientific]
日期:2021-07-21
卷期号:30 (01)
被引量:4
标识
DOI:10.1142/s0218348x21502364
摘要
In this paper, we consider the trapping problem on the level-3 Sierpinski gasket [Formula: see text] when both nearest-neighbor (NN) and non-nearest-neighbor (NNN) jumps are included. Based on the topological structure of network and the method of probability generation function, we get the analytical expression of the average trapping time (ATT). Therefore, compared with the case where only NN jumps are allowed, we modify the specific value of ATT. Our results prove the exponent of the scaling expression has nothing to do with the NNN jump probability [Formula: see text], i.e. the scaling expression of ATT still scales superlinearly with the large network size. According to the analytical expression, we do numerical simulations of ATT with respect to parameters [Formula: see text] and [Formula: see text] (the number of generations of the network). The results show that the ATT will decrease with the increase of [Formula: see text]. These also indicate that NNN jump is helpful to improve the efficiency of random walk on network [Formula: see text].
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