Estimating degree rank in complex networks

  • Akrati Saxena
  • Ralucca Gera
  • S. R. S. Iyengar
Original Article


Identifying top-ranked nodes can be performed using different centrality measures, based on their characteristics and influential power. The most basic of all the ranking techniques is based on nodes degree. While finding the degree of a node requires local information, ranking the node based on its degree requires global information, namely the degrees of all the nodes of the network. It is infeasible to collect the global information for some graphs such as (i) the ones emerging from big data, (ii) dynamic networks, and (iii) distributed networks in which the whole graph is not known. In this work, we propose methods to estimate the degree rank of a node, that are faster than the classical method of computing the centrality value of all nodes and then rank a node. The proposed methods are modeled based on the network characteristics and sampling techniques, thus not requiring the entire network. We show that approximately \(1\%\) node samples are adequate to find the rank of a node with high accuracy.


Degree centrality Ranking nodes Social network analysis Sampling techniques 



Gera would like to thank the DoD for sponsoring this work. Saxena and Iyengar would like to thank IIT Ropar HPC committee for providing the resources to perform the experiments.


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Copyright information

© This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2018

Authors and Affiliations

  1. 1.Department of CSEIndian Institute of Technology RoparRupnagarIndia
  2. 2.Department of Applied MathematicsNaval Postgraduate SchoolCAUSA

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