Predicting Hierarchical Structure in Small World Social Networks

Typical analytical measures in graph theory like degree centrality, betweenness and closeness centralities are very common and have long history of their successful use. However, modeling of covert, terrorist or criminal networks through social graphs do not really provide the hierarchical structure of such networks because these networks are composed of leaders and followers. In this short paper we investigate small world networks by computing first the Bayes posteriori probability which is then used to calculate the entropy of the network. The computed probability and entropy distribution further utilized in predicting the command structure of the network.