Skip to main content
Springer Nature Link
Log in

A new centrality measure based on sub-tree

  • Research Article
  • Published:
Frontiers of Computer Science in China Aims and scope Submit manuscript

Abstract

How to evaluate the importance of nodes in networks and detect the centrality has become a vital problem in improving the efficiency of telecommunication and making a disease immunity strategy. We consider the mechanisms of real networks, and define a cost function to describe different hierarchies of networks to measure node importance. This method takes up a node’s regional influence as well as its global influence to evaluate its importance. The results of simulation prove that this method is proper to describe effectively and detect node discrepancies in a network.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

References

  1. Watts D, Strogatz S. Collective dynamics of small-world networks. Nature, 1998, 393(6684): 440–442

    Article  Google Scholar 

  2. Albert R, Jeong H, Barabas A L. Error and attack tolerance of complex networks. Nature, 2000, 406: 378–382

    Article  Google Scholar 

  3. Barabasi A L, Albert R. Emergence of scaling in random networks. Science, 1999, 286(5439): 509–512

    Article  MathSciNet  Google Scholar 

  4. Albert R, Jeong H, Barabasi A L. Diameter of the world-wide web. Nature, 1999, 401: 130–131

    Article  Google Scholar 

  5. Albert R, Barabasi A L. Statistical mechanics of complex networks. Reviews of Modern Physics, 2002, 74: 47–97

    Article  MathSciNet  Google Scholar 

  6. Newman M. The structure and function of complex networks. SIAM Review, 2003, 45: 167–256

    Article  MATH  MathSciNet  Google Scholar 

  7. Goh K, Oh E, Kahng B, Kim D. Betweenness centrality correlation in social networks. Physical Review E, 2003, 67: 017101

    Article  Google Scholar 

  8. Albert R, Barabasi A L. Topology of evolving networks: local events and university. Physical Review Letters, 2000, 85(24): 5234–5237

    Article  Google Scholar 

  9. Freeman L. A set of measures of centrality based upon betweenness. Sociometry, 1977, 40(1): 35–41

    Article  Google Scholar 

  10. Freeman L. Centrality in social networks: conceptual clarification. Social Networks, 1979, 1(3): 215–219

    Article  Google Scholar 

  11. Freeman L, Roeder D, Mulholland R R. Centrality in social networks: ii. experimental results. Social Networks, 1979, 2: 119–141

    Article  Google Scholar 

  12. Brandes U. A fast algorithm for betweenness centrality. Journal of Mathematical Sociology, 2001, 25(2): 163–171

    MATH  Google Scholar 

  13. Estrada E, Rodriguez-Velazquez J A. Subgraph centrality in complex networks. Physical Review E, 2005, 71: 056103

    Article  MathSciNet  Google Scholar 

  14. Newman M. A measure of betweenness centrality based on random walk. Social Networks, 2005, 27(1): 39–54

    Article  Google Scholar 

  15. Klovdahl A S. Social networks and the spread of infectious diseases: the AIDS example. Social Science & Medicine, 1985, 21(11): 1203–1216

    Article  Google Scholar 

  16. Barthelemy M. Betweenness centrality in large complex networks. The European Physical Journal B, 2004, 38(2): 163–168

    Article  Google Scholar 

  17. Tan Y J, Wu J, Deng H Z. Evaluation method for node importance based on node contraction in complex networks. System Engineering-Theory & Practice, 2006, (11): 26–35

Download references

Author information

Authors and Affiliations

  1. Institute of System Engineering, Dalian University of Technology, Dalian, 116023, China

    Lili Rong, Tianzhu Guo & Jiyong Zhang

Authors
  1. Lili Rong
    View author publications

    Search author on:PubMed Google Scholar

  2. Tianzhu Guo
    View author publications

    Search author on:PubMed Google Scholar

  3. Jiyong Zhang
    View author publications

    Search author on:PubMed Google Scholar

Corresponding author

Correspondence to Lili Rong.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Rong, L., Guo, T. & Zhang, J. A new centrality measure based on sub-tree. Front. Comput. Sci. China 3, 356–360 (2009). https://doi.org/10.1007/s11704-009-0046-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11704-009-0046-y

Keywords