Skip to main content

Online Sampling of High Centrality Individuals in Social Networks

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNAI,volume 6118)

Abstract

In this work, we investigate the use of online or “crawling” algorithms to sample large social networks in order to determine the most influential or important individuals within the network (by varying definitions of network centrality). We describe a novel sampling technique based on concepts from expander graphs. We empirically evaluate this method in addition to other online sampling strategies on several real-world social networks. We find that, by sampling nodes to maximize the expansion of the sample, we are able to approximate the set of most influential individuals across multiple measures of centrality.

Keywords

  • Social Network
  • Centrality Measure
  • Original Network
  • Closeness Centrality
  • Jaccard Similarity

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-642-13657-3_12
  • Chapter length: 8 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   99.00
Price excludes VAT (USA)
  • ISBN: 978-3-642-13657-3
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   129.99
Price excludes VAT (USA)

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Freeman, L.C.: Centrality in social networks. Social Networks 1, 215–239 (1979)

    CrossRef  Google Scholar 

  2. Wasserman, S., Faust, K., Iacobucci, D.: Social Network Analysis: Methods and Applications. Cambridge University Press, Cambridge (November 1994)

    Google Scholar 

  3. Bavelas, A.: Communication patterns in task-oriented groups. J. Acoustical Soc. of Am. 22(6), 725–730 (1950)

    CrossRef  Google Scholar 

  4. Russo, T., Koesten, J.: Prestige, centrality, and learning: A social network analysis of an online class. Communication Education 54(3), 254–261 (2005)

    CrossRef  Google Scholar 

  5. Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: Bringing order to the web. Technical report, Stanford InfoLab (1998)

    Google Scholar 

  6. Anthonisse, J.: The rush in a graph. Mathematische Centrum, Amsterdam (1971)

    Google Scholar 

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

    CrossRef  Google Scholar 

  8. Bonacich, P.: Power and centrality: A family of measures. American J. Sociology 92(5), 1170–1182 (1987)

    CrossRef  Google Scholar 

  9. Boldi, P., Santini, M., Vigna, S.: Paradoxical effects in pagerank incremental computations. In: Workshop on Web Graphs (2004)

    Google Scholar 

  10. Abiteboul, S., Preda, M., Cobena, G.: Adaptive on-line page importance computation. In: WWW (2003)

    Google Scholar 

  11. Cho, J., Molina, H.G., Page, L.: Efficient crawling through url ordering. Computer Networks and ISDN Systems 30(1-7), 161–172 (1998)

    CrossRef  Google Scholar 

  12. Najork, M.: Breadth-first search crawling yields high-quality pages. In: WWW 2001 (2001)

    Google Scholar 

  13. Leskovec, J., Kleinberg, J., Faloutsos, C.: Graphs over time: densification laws, shrinking diameters and possible explanations. In: KDD 2005 (2005)

    Google Scholar 

  14. Krishnamurthy, V., Faloutsos, M., Chrobak, M., Cui, J., Lao, L., Percus, A.: Sampling large internet topologies for simulation purposes. Computer Networks 51(15), 4284–4302 (2007)

    CrossRef  Google Scholar 

  15. Hubler, C., Kriegel, H.P., Borgwardt, K., Ghahramani, Z.: Metropolis algorithms for representative subgraph sampling. In: ICDM 2008 (2008)

    Google Scholar 

  16. Hoory, S., Linial, N., Wigderson, A.: Expander graphs and their applications. Bull. Amer. Math. Soc. 43, 439–561 (2006)

    MATH  CrossRef  MathSciNet  Google Scholar 

  17. Leskovec, J., Kleinberg, J., Faloutsos, C.: Graph evolution: Densification and shrinking diameters. ACM TKDD 1(1), 2 (2007)

    CrossRef  Google Scholar 

  18. Shetty, J., Adibi, J.: Enron email dataset. Technical report (2004)

    Google Scholar 

  19. Richardson, M., Agrawal, R., Domingos, P.: Trust Management for the Semantic Web. In: Fensel, D., Sycara, K., Mylopoulos, J. (eds.) ISWC 2003. LNCS, vol. 2870, pp. 351–368. Springer, Heidelberg (2003)

    Google Scholar 

  20. Leskovec, J., Lang, K.J., Dasgupta, A., Mahoney, M.W.: Statistical properties of community structure in large social and information networks. In: WWW 2008 (2008)

    Google Scholar 

  21. Kendall, M., Gibbons, J.D.: Rank Correlation Methods, 5th edn. (September 1990)

    Google Scholar 

  22. Jaccard, P.: Étude comparative de la distribution florale dans une portion des alpes et des jura. Bulletin del la Société Vaudoise des Sciences Naturelles 37, 547–579 (1901)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Maiya, A.S., Berger-Wolf, T.Y. (2010). Online Sampling of High Centrality Individuals in Social Networks. In: Zaki, M.J., Yu, J.X., Ravindran, B., Pudi, V. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2010. Lecture Notes in Computer Science(), vol 6118. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13657-3_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-13657-3_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13656-6

  • Online ISBN: 978-3-642-13657-3

  • eBook Packages: Computer ScienceComputer Science (R0)