Skip to main content
SpringerLink
Log in

Infectious Communities Forging

Using Information Diffusion Model in Social Network Mining

  • Conference paper
Web Information Systems and Mining (WISM 2011)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6988))

Included in the following conference series:

  • 1290 Accesses

Abstract

This article proposes a new model for clustering individual nodes based on node’s interrelation with a real-life mining application. The model is capable of detecting a network topology based on information flow and therefore could be easily extended and applied in a variety of today’s research fields. E.g. discover audience group sharing similar attitude, or retrieve authors’ academic referencing group or plot active friend society in social networks. An effective algorithm: Boundary Growth Algorithm is proposed through which people can find the underlying structure of networks. Extensive experimental evaluations demonstrate the effectiveness of our approach.

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

Access this chapter

Log in via an institution
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. Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. Natl. Acad. Sci., 8271–8276 (2002)

    Google Scholar 

  2. Hopcroft, J., Khan, O., Kulis, B., Selman, B.: Natural communities in large linked networks. In: Proc. KDD 2003, pp. 541–546 (2003)

    Google Scholar 

  3. Newman, M., Barabasi, A.-L., Watts, D.J. (eds.): The Structure and Dynamics of Networks. Princeton University Press, Princeton (2006)

    Google Scholar 

  4. Newman, M.E.J.: Detecting community structure in networks. Proc. Natl. Acad. Sci. 99, 7821–7826 (2002)

    Article  MATH  Google Scholar 

  5. Wang, X., Tang, L., Gao, H., Liu, H.: Discovering Overlapping Groups in Social Media. In: IEEE International Conference on Data Mining, ICDM 2010, pp. 569–578 (2010)

    Google Scholar 

  6. Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small world’ networks. Nature 393(6684), 400–422 (1988)

    Google Scholar 

  7. Ahn, Y.-Y., Bagrow, J.P., Lehmann, S.: Linke communities reveal multi-scale complexity in networks (2009)

    Google Scholar 

  8. Rogers, E.: Diffusion of Innovations, 4th edn. Free Press, New York (1995)

    Google Scholar 

  9. Ryan, B., Gross, N.C.: The diffusion of hybrid seed corn in two Iowa communities. Rural Sociology, 15–24 (1943)

    Google Scholar 

  10. Coleman, J., Menzel, H., Katz, E.: Medical Innovations: A Diffusion Study. Bobbs Merrill (1966)

    Google Scholar 

  11. Easley, D., Kleinberg, J.: Networks, Crowds, and Markets: Reasoning About a Highly Connected World. Cambridge University Press, Cambridge (2010)

    Book  MATH  Google Scholar 

  12. Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., VanBriensen, J., Glance, N.: Cost-effective Outbreak Detection in Networks. In: Proc. KDD 2007, pp. 420–429 (2007)

    Google Scholar 

  13. Rodriguez, M.G., Leskovec, J., Krause, A.: Inferring Networks of Diffusion and Influence. In: Proc. KDD 2010, pp. 1019–1028 (2010)

    Google Scholar 

  14. Yang, J., Leskovec, J.: Modeling Information Diffusion In Implicit Networks. In: 2010 IEEE International Conference on Data Mining ICDM, pp. 599–608 (2010)

    Google Scholar 

  15. MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability (1967)

    Google Scholar 

  16. Dempster, A.P., Laird, N.M., Rubin, D.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society Series B 39(1), 1–38 (1977)

    MathSciNet  MATH  Google Scholar 

  17. Weiss, Y.: Segmentation using eigenvectors: A unifying view. In: Proceedings of International Conference on Computer Vision (1999)

    Google Scholar 

  18. Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Physical Review E 69, 26113 (2004)

    Article  Google Scholar 

  19. Alpert, C., Kahng, A., Yao, S.: Spectral partitioning: The more eigenvectors, the better. Discrete Applied Math. 90, 3–26 (1999)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong S.A.R.

    Tianran Hu & Xuechen Feng

Authors
  1. Tianran Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xuechen Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer and Inforamtion Science, University of Macau, Av. Padre Tomás Pereira, Taipa, Macau, China

    Zhiguo Gong

  2. School of Computer, Shanghai University, 200444, Shanghai, China

    Xiangfeng Luo

  3. College of Computer and Software, Taiyuan University of Technology, 030024, Taiyuan, China

    Junjie Chen

  4. School of Computer and Information Engineering, Shanghai University of Electric Power, 200090, Shanghai, China

    Jingsheng Lei

  5. Department of Business Administration, Caritas Institute of Higher Education, 18 Chui Ling Road, Tseung Kwan O, Hong Kong, China

    Fu Lee Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Hu, T., Feng, X. (2011). Infectious Communities Forging. In: Gong, Z., Luo, X., Chen, J., Lei, J., Wang, F.L. (eds) Web Information Systems and Mining. WISM 2011. Lecture Notes in Computer Science, vol 6988. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23982-3_33

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-23982-3_33

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23981-6

  • Online ISBN: 978-3-642-23982-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation