We investigate searching efficiency of different kinds of random walk on complex networks which rely on local information and one-step memory. For the studied navigation strategies we obtained theoretical and numerical values for the graph mean first passage times as an indicator for the searching efficiency. The experiments with generated and real networks show that biasing based on inverse degree, persistence and local two-hop paths can lead to smaller searching times. Moreover, these biasing approaches can be combined to achieve a more robust random search strategy. Our findings can be applied in the modeling and solution of various real-world problems.

翻译：本研究考察了复杂网络上依赖局部信息和单步记忆的不同类型随机游走的搜索效率。针对所研究的导航策略，我们获得了图平均首次通过时间的理论与数值结果，以此作为搜索效率的指标。在生成网络和真实网络上的实验表明，基于逆度、持续性和局部两跳路径的偏置机制能够有效缩短搜索时间。此外，这些偏置方法可相互结合，形成更具鲁棒性的随机搜索策略。本研究成果可应用于各类实际问题的建模与求解。