Centrality Prediction in Mobile Social Networks
By analyzing evolving centrality roles using time dependent graphs, researchers may predict future centrality values. This may prove invaluable in designing efficient routing and energy saving strategies and have profound implications on evolving social behavior in dynamic social networks. In this paper, the authors propose a new method to predict centrality values of nodes in a dynamic environment. The proposed paper is based on calculating the correlation between current and past measure of centrality for each corresponding node, which is used to form a composite vector to represent the given state of centralities.