We propose one-step memory random walk on complex networks for which at each time step, the walker will not be allowed to revisit the last position. Mean first passage time is adopted to quantify its search efficiency and a procedure is provided for calculating it analytically. Interestingly, we find that in the same circumstance, one-step memory random walk usually takes less time than random walk for finding a target given in advance. Furthermore, this navigation strategy presents a better performance even in comparison with corresponding optimal biased random walk when moving on networks without small-world effect. Our findings are confirmed on two canonical network models and a number of real networks. Our work reveals that one-step memory random walk is an efficient local search strategy, which can be applied to transportation and information spreading.

中文翻译：

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复杂网络上的一步记忆随机游走：一种有效的局部导航策略

我们提出了在复杂网络上的一步记忆随机游走，在每个时间步，walker 都不会被允许重新访问最后一个位置。采用平均首次通过时间来量化其搜索效率，并给出了解析计算的程序。有趣的是，我们发现在相同的情况下，一步记忆随机游走通常比随机游走花费更少的时间来找到预先给定的目标。此外，即使在没有小世界效应的网络上移动时，与相应的最优有偏随机游走相比，这种导航策略也表现出更好的性能。我们的发现在两个规范网络模型和一些真实网络上得到了证实。我们的工作表明，一步记忆随机游走是一种有效的局部搜索策略，