Fast Estimation of Closeness Centrality Ranking
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Authors
Saxena, Akrati
Gera, Ralucca
Iyengar, S.R.S.
Subjects
Advisors
Date of Issue
2017-08-03
Date
Publisher
ACM
Language
Abstract
Closeness centrality is one way of measuring how central a node is in the given network. The closeness centrality measure assigns a centrality value to each node based on its accessibility to the whole network. In real life applications, we are mainly interested in ranking nodes based on their centrality values. The classical method to compute the rank of a node first computes the closeness centrality of all nodes and then compares them to get its rank. Its time complexity is O(n · m + n), where n represents total number of nodes, and m represents total number of edges in the network. In the present work, we propose a heuristic method to fast estimate the closeness rank of a node in O(α · m) time complexity, where α = 3. We also propose an extended improved method using uniform sampling technique. This method better estimates the rank and it has the time complexity O(α · m), where α ≈ 10-100. This is an excellent improvement over the classical centrality ranking method. The efficiency of the proposed methods is verified on real world scale-free social networks using absolute and weighted error functions.
Type
Article
Description
The article of record as published may be found at http://dx.doi.org/10.1145/3110025.3110064
Series/Report No
Department
Applied Mathematics
Organization
Naval Postgraduate School (U.S.)
Identifiers
NPS Report Number
Sponsors
Asymmetric Warfare Group
West Point Network Science Center
West Point Network Science Center
Funder
Format
6 p.
Citation
Saxena, Akrati, Ralucca Gera, and S. R. S. Iyengar. "Fast estimation of closeness centrality ranking." In Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 80-85. ACM, 2017.
Distribution Statement
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.