short-paper

Fast Estimation of Closeness Centrality Ranking

Authors:

S. R. S. IyengarAuthors Info & Claims

ASONAM '17: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017

Pages 80 - 85

https://doi.org/10.1145/3110025.3110064

Published: 31 July 2017 Publication History

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.

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Cited By

Saxena RPaira PJadeja M(2024)CLUS-BET: improving influence propagation and classification in networks using a novel seed selection techniqueSocial Network Analysis and Mining10.1007/s13278-024-01244-714:1Online publication date: 12-Apr-2024
https://doi.org/10.1007/s13278-024-01244-7
Srivastava AMishra R(2024)What is my privacy score? Measuring users’ privacy on social networking websitesElectronic Commerce Research10.1007/s10660-023-09796-0Online publication date: 1-Feb-2024
https://doi.org/10.1007/s10660-023-09796-0
Plana FAbeliuk APérez J(2024)Quickcent: a fast and frugal heuristic for harmonic centrality estimation on scale-free networksComputing10.1007/s00607-024-01303-z106:8(2675-2705)Online publication date: 8-Jun-2024
https://doi.org/10.1007/s00607-024-01303-z
Show More Cited By

Fast Estimation of Closeness Centrality Ranking
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
2. Information systems
  1. Information systems applications

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cover image ACM Conferences

ASONAM '17: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017

July 2017

698 pages

ISBN:9781450349932

DOI:10.1145/3110025

Copyright © 2017 ACM.

© 2017 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the United States Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Published: 31 July 2017

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ASONAM '17: Advances in Social Networks Analysis and Mining 2017

July 31 - August 3, 2017

Sydney, Australia

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Cited By

Saxena RPaira PJadeja M(2024)CLUS-BET: improving influence propagation and classification in networks using a novel seed selection techniqueSocial Network Analysis and Mining10.1007/s13278-024-01244-714:1Online publication date: 12-Apr-2024
https://doi.org/10.1007/s13278-024-01244-7
Srivastava AMishra R(2024)What is my privacy score? Measuring users’ privacy on social networking websitesElectronic Commerce Research10.1007/s10660-023-09796-0Online publication date: 1-Feb-2024
https://doi.org/10.1007/s10660-023-09796-0
Plana FAbeliuk APérez J(2024)Quickcent: a fast and frugal heuristic for harmonic centrality estimation on scale-free networksComputing10.1007/s00607-024-01303-z106:8(2675-2705)Online publication date: 8-Jun-2024
https://doi.org/10.1007/s00607-024-01303-z
Isravel DSilas SRajsingh E(2021)Centrality Based Congestion Detection Using Reinforcement Learning Approach for Traffic Engineering in Hybrid SDNJournal of Network and Systems Management10.1007/s10922-021-09627-330:1Online publication date: 14-Sep-2021
https://doi.org/10.1007/s10922-021-09627-3
Semenkovich STsukanova O(2019)On the Algorithms of Identifying Opinion Leaders in Social NetworksProcedia Computer Science10.1016/j.procs.2019.12.050162(778-785)Online publication date: 2019
https://doi.org/10.1016/j.procs.2019.12.050
Mahyar HHasheminezhad RStanley H(2019)Compressive closeness in networksApplied Network Science10.1007/s41109-019-0213-54:1Online publication date: 6-Nov-2019
https://doi.org/10.1007/s41109-019-0213-5
Ji JWu GDuan CRen YWang Z(2019)Greedily Remove k Links to Hide Important Individuals in Social NetworkSecurity and Privacy in Social Networks and Big Data10.1007/978-981-15-0758-8_17(223-237)Online publication date: 24-Oct-2019
https://doi.org/10.1007/978-981-15-0758-8_17
Saxena AGera RIyengar S(2018)A heuristic approach to estimate nodes’ closeness rank using the properties of real world networksSocial Network Analysis and Mining10.1007/s13278-018-0545-79:1Online publication date: 5-Dec-2018
https://doi.org/10.1007/s13278-018-0545-7
Mahyar HHasheminezhad RGhalebi EGrosu RStanley H(2018)A Compressive Sensing Framework for Distributed Detection of High Closeness Centrality Nodes in NetworksComplex Networks and Their Applications VII10.1007/978-3-030-05414-4_8(91-103)Online publication date: 5-Dec-2018
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Saxena AIyengar S(2018)K-Shell Rank Analysis Using Local InformationComputational Data and Social Networks10.1007/978-3-030-04648-4_17(198-210)Online publication date: 18-Nov-2018
https://doi.org/10.1007/978-3-030-04648-4_17

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