Skip to main content

Centrality and Spectral Radius in Dynamic Communication Networks

  • Conference paper
Computing and Combinatorics (COCOON 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7936))

Included in the following conference series:

Abstract

We explore the influence of the choice of attenuation factor on Katz centrality indices for evolving communication networks. For given snapshots of a network observed over a period of time, recently developed communicability indices aim to identify best broadcasters and listeners in the network. In this article, we looked into the sensitivity of communicability indices on the attenuation factor constraint, in relation to spectral radius (the largest eigenvalue) of the network at any point in time and its computation in the case of large networks. We proposed relaxed communicability measures where the spectral radius bound on attenuation factor is relaxed and the adjacency matrix is normalised in order to maintain the convergence of the measure. Using a vitality based measure of both standard and relaxed communicability indices we looked at the ways of establishing the most important individuals for broadcasting and receiving of messages related to community bridging roles. We illustrated our findings with two examples of real-life networks, MIT reality mining data set of daily communications between 106 individuals during one year and UK Twitter mentions network, direct messages on Twitter between 12.4k individuals during one week.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bonacich, P.: Power and centrality: A family of measures. American Journal of Sociology 92, 1170–1182 (1987)

    Article  Google Scholar 

  2. Bonacich, P., Lloyd, P.: Eigenvector-like measures of centrality for asymmetric relations. Social Networks 23(3), 191–201 (2001)

    Article  Google Scholar 

  3. Borgatti, S.P., Everett, M.G.: A graph-theoretic perspective on centrality. Social Networks 28(4), 466–484 (2006)

    Article  Google Scholar 

  4. Burt, R.S.: Brokerage and closure: An introduction to social capital. Eur. Sociol. Rev. 23(5), 666–667 (2007)

    Article  Google Scholar 

  5. Castellano, C., Pastor-Satorras, R.: Thresholds for epidemic spreading in networks. Physical Review Letters 105, 218701 (2010)

    Article  Google Scholar 

  6. Crofts, J.J., Higham, D.J.: Googling the brain: Discovering hierarchical and asymmetric network structures, with applications in neuroscience. Internet Mathematics (Special Issue on Biological Networks) (2011)

    Google Scholar 

  7. Das, K.C., Kumar, P.: Some new bounds on the spectral radius of graphs. Discrete Mathematics 281(1-3), 149–161 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  8. Eagle, N., Pentland, A.S., Lazer, D.: Inferring friendship network structure by using mobile phone data. Proceedings of the National Academy of Sciences 106, 15274–15278 (2009)

    Article  Google Scholar 

  9. Estrada, E., Hatano, N.: Communicability in complex networks. Physical Review E 77 (2008)

    Google Scholar 

  10. Gantmacher, F.: The Theory of Matrices, vol. 2. AMS Chelsea Publishing (2000)

    Google Scholar 

  11. Ghosh, R., Lerman, K.: Parameterized centrality metric for network analysis. Physical Review E 83(6), 066118+ (2011)

    Article  Google Scholar 

  12. Grindrod, P., Higham, D.J.: Models for evolving networks: with applications in telecommunication and online activities. IMA Journal of Management Mathematics (2011)

    Google Scholar 

  13. Grindrod, P., Higham, D.J., Parsons, M.C., Estrada, E.: Communicability across evolving networks. Physical Review E 83 (2011)

    Google Scholar 

  14. Jamaković, A., Kooij, R.E., Van Mieghem, P., van Dam, E.R.: Robustness of networks against viruses: the role of the spectral radius. In: Symposium on Communications and Vehicular Technology, pp. 35–38 (November 2006)

    Google Scholar 

  15. Katz, L.: A new index derived from sociometric data analysis. Psychometrika 18, 39–43 (1953)

    Article  MATH  Google Scholar 

  16. Opsahl, T., Agneessens, F., Skvoretz, J.: Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks 32(3), 245 (2010)

    Article  Google Scholar 

  17. Valente, T.: Network interventions. Science 337(6090) (2012)

    Google Scholar 

  18. Wang, Y., Chakrabarti, D., Wang, C., Faloutsos, C.: Epidemic spreading in real networks: An eigenvalue viewpoint. In: SRDS, pp. 25–34 (2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Vukadinović Greetham, D., Stoyanov, Z., Grindrod, P. (2013). Centrality and Spectral Radius in Dynamic Communication Networks. In: Du, DZ., Zhang, G. (eds) Computing and Combinatorics. COCOON 2013. Lecture Notes in Computer Science, vol 7936. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38768-5_72

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-38768-5_72

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38767-8

  • Online ISBN: 978-3-642-38768-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics