Human navigation has been a topic of interest in spatial cognition from the past few decades. It has been experimentally observed that humans accomplish the task of way-finding a destination in an unknown environment by recognizing landmarks. Investigations using network analytic techniques reveal that humans, when asked to way-find their destination, learn the top ranked nodes of a network. In this paper we report a study simulating the strategy used by humans to recognize the centers of a network. We show that the paths obtained from our simulation has the same properties as the paths obtained in human based experiment. The simulation thus performed leads to a novel way of path-finding in a network. We discuss the performance of our method and compare it with the existing techniques to find a path between a pair of nodes in a network.
Many large distributed systems can be characterized as networks where short paths exist between nearly every pair of nodes. These include social, biological, communication, and distribution networks, which often display power-law or small-world structure. A central challenge of distributed systems is directing messages to specific nodes through a sequence of decisions made by individual nodes without global knowledge of the network. We present a probabilistic analysis of this navigation problem that produces a surprisingly simple and effective method for directing messages. This method requires calculating only the product of the two measures widely used to summarize all local information. It outperforms prior approaches reported in the literature by a large margin, and it provides a formal model that may describe how humans make decisions in sociological studies intended to explore the social network as well as how they make decisions in more naturalistic settings.