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Analytical results for the distribution of first hitting times of random walks on random regular graphs

机译:在随机常规图表上的第一次击中随机散步时间的分析结果

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摘要

We present analytical results for the distribution of first hitting (FH) times of random walks (RWs) on random regular graphs (RRGs) of degree c > 3 and a finite size N. Starting from a random initial node at time t = 1, at each time step t > 2 an RW hops randomly into one of the c neighbors of its previous node. In some of the time steps the RW may hop into a yet-unvisited node while in other time steps it may revisit a node that has already been visited before. The first time at which the RW enters a node that has already been visited before is called the FH time or the first intersection length. The FH event may take place either by backtracking (BT) to the previous node or by retracing (RET), namely stepping into a node which has been visited two or more time steps earlier. We calculate the tail distribution P(T-FH > t) of FH times as well as its mean ⟨T-FH⟩ and variance Var(T-FH). We also calculate the probabilities P-BT and P-RET that the FH event will occur via the BT scenario or via the RET scenario, respectively. We show that in dilute networks the dominant FH scenario is BT while in dense networks the dominant scenario is RET and calculate the conditional distributions P(T-FH = t|BT) and P(T-FH = t|RET), for the two scenarios. The analytical results are in excellent agreement with the results obtained from computer simulations. Considering the FH event as a termination mechanism of the RW trajectories, these results provide useful insight into the general problem of survival analysis and the statistics of mortality rates when two or more termination scenarios coexist.
机译:我们给出了随机游动(RW)的首次命中(FH)次数在c>3次且有限大小N的随机正则图(RRG)上的分布的分析结果。从时间t=1的随机初始节点开始,在每个时间步长t>2时,RW随机跳到其前一个节点的一个c邻域中。在一些时间步中,RW可能会跳入尚未访问的节点,而在其他时间步中,RW可能会重新访问之前已经访问过的节点。RW第一次进入之前已经访问过的节点的时间称为FH时间或第一个交叉口长度。FH事件可以通过回溯(BT)到前一个节点或通过回溯(RET)来发生,即进入之前已经访问了两个或更多时间步的节点。我们计算了跳频次数的尾部分布P(T-FH>T)及其均值和左角括号;T-FH和直角支架;方差Var(T-FH)。我们还分别计算了通过BT场景或通过RET场景发生FH事件的概率P-BT和P-RET。我们证明了在稀疏网络中,主要的FH场景是BT,而在密集网络中,主要的场景是RET,并计算了这两种场景的条件分布P(T-FH=T | BT)和P(T-FH=T | RET)。分析结果与计算机模拟结果非常吻合。考虑到FH事件是RW轨迹的终止机制,这些结果为生存分析的一般问题以及两种或两种以上终止情景共存时的死亡率统计提供了有用的见解。

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