智能随机游走器：知道路径的代价
Smart random walkers: the cost of knowing the path
摘要：
在这项工作中，我们研究了网络中的目标信号的问题，使用熵信息测量来量化目标的成本。我们引入了一个惩罚规则，对长路径施加限制，从而将信号集中到目标。通过这个方案，我们不断地从完全随机的步行者到偏向目标的步行者。我们发现最优惩罚度主要由网络的拓扑结构决定。通过分析几个例子，我们发现少量的惩罚大大减少了典型的步行长度，由此我们得出结论，网络可以有效地导航有限的信息。
英文摘要：
In this work we study the problem of targeting signals in networks using entropy information measurements to quantify the cost of targeting. We introduce a penalization rule that imposes a restriction to the long paths and therefore focus the signal to the target. By this scheme we go continuously from fully random walkers to walkers biased to the target. We found that the optimal degree of penalization is mainly determined by the topology of the network. By analyzing several examples, we have found that a small amount of penalization reduces considerably the typical walk length, and from this we conclude that a network can be efficiently navigated with restricted information.
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。
