  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.
