Abstract. 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 the degree centrality ranking of a node without having complete structure of the graph. The proposed algorithm 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 the average error in the estimated ranking is approximately 5% of the total number of nodes.
We further compute the variance in the rank estimation using power law degree distribution REF .
Estimating the Degree Centrality Ranking of a Node
ArXiv
ArXiv
Computer Science Physics