Estimating the degree centrality ranking

Complex networks have gained more attention from the last few years. The size of the real world complex networks, such as online social networks, WWW networks, collaboration networks, is exponentially increasing with time. It is not feasible to completely collect, store and process these networks. In the present work, we propose a method to estimate degree centrality ranking of a node without having complete structure of the graph. The proposed method uses degree of a node and power law exponent of the degree distribution to calculate the ranking. We also study simulation results on Barabasi-Albert model. Simulation results show that average error in the calculated ranking is approximately 5% of total number of nodes.