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.
It is easier to find short chains between points in some networks than others. The small-world phenomenon — the principle that most of us are linked by short chains of acquaintances — was first investigated as a question in sociology1,2 and is a feature of a range of networks arising in nature and technology3,4,5. Experimental study of the phenomenon1 revealed that it has two fundamental components: first, such short chains are ubiquitous, and second, individuals operating with purely local information are very adept at finding these chains. The first issue has been analysed2,3,4, and here I investigate the second by modelling how individuals can find short chains in a large social network.