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, Volume 22, Issue 6, pp 3047–3064 | Cite as

Parallel computation of hierarchical closeness centrality and applications

  • Hai Jin
  • Chen Qian
  • Dongxiao YuEmail author
  • Qiang-Sheng Hua
  • Xuanhua Shi
  • Xia Xie
Part of the following topical collections:
  1. Special Issue on Social Computing and Big Data Applications


It has long been an area of interest to identify important vertices in social networks. Closeness centrality is one of the most popular measures of centrality of vertices. Generally speaking, it measures how a node is close to all other nodes on average. However, closeness centrality measures the centrality from a global view. Consequently, in real-world networks that is normally composed by some communities connected, using closeness centrality may suffer from the flaw that local central vertices within communities are neglected. To resolve this issue, we propose a new centrality measure, Hierarchical Closeness Centrality (HCC), to depict the local centrality of vertices. Experiments show that comparing with closeness centrality, HCC is a better index in finding most influential vertices and community detection. Furthermore, we present a parallel algorithm for HCC computation, by well analyzing the independence between vertices in the computation procedure. Extensive experiments on real-world datesets demonstrate that the parallel algorithm can greatly reduce the computation time compared to trivial algorithms.


Data mining Closeness centrality Parallel algorithm 



This work is supported by the National Key Research and Development Program of China No.2017YFC0803700, the National Natural Science Foundation of China Grants 61602195, 61572216, 61433019 and U1435217, Natural Science Foundation of Hubei Province 2017CFB301, the Outstanding Youth Foundation of Hubei Province 2016CFA032, and Fundamental Research Funds for HUST.


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Services Computing Technology and System Lab, Big Data Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhanPeople’s Republic of China

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