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.
Kleinberg's work @cite_17 on navigation in a small world, was the first paper to shed light on the navigation problem on complex networks. The paper highlighted the fact that it is easier to find short chains between points in networks than .
@cite_17
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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.