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.
Centrality measures have been defined to quantify the importance of a node in complex networks. The relative importance of a node can be measured using its centrality rank based on the centrality value. In the present work, we predict the degree centrality rank of a node without having the entire network. The proposed method uses degree of the node and some network parameters to predict its rank. These network parameters include network size, minimum, maximum, and average degree of the network. These parameters are estimated using random walk sampling techniques. The proposed method is validated on Barabasi-Albert networks. Simulation results show that the proposed method predicts the rank of higher degree nodes with more accuracy. The average error in the rank prediction is approximately 0.16% of the network size.
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