[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content
Springer Nature Link
Log in

A Bayesian Graph Clustering Approach Using the Prior Based on Degree Distribution

  • Conference paper
Neural Information Processing (ICONIP 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5863))

Included in the following conference series:

  • 1514 Accesses

Abstract

Newman et al. proposed a stochastic graph clustering approach using a mixture model with an assumption that a group of vertices is regarded as a class when the vertices have a similar connection pattern. Kuwata et al. recently adopted a nonparametric Bayesian approach and improved Newman’s one in such a way that the number of classes can also be empirically estimated. In this paper, we propose a new approach that can incorporate the degree distribution of the network structure as priors for Bayesian estimation. We show the effectiveness of our method through experiments using both artificial and real data.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Barabasi, A.-L.: Linked: The New Science of Networks. Perseus Books, Cambridge (2002)

    Google Scholar 

  2. Newman, M.E.J.: The structure and function of complex networks. SIAM Review 45, 167–256 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  3. Danon, L., Duch, J., Diaz-Guilera, A., Arenas, A.: Comparing community structure identification. J. Stat. Mech., P09008 (2005)

    Google Scholar 

  4. Fortunato, S., Castellano, C.: Community structure in graphs. In: Encyclopedia of Complexity and System Science. Springer, Heidelberg (2009)

    Google Scholar 

  5. Newman, M.E.J., Leicht, E.A.: Mixture models and exploratory data analysis in networks. Proc. Natl. Acad. Sci. USA 104, 9564–9569 (2007)

    Article  MATH  Google Scholar 

  6. Kuwata, S., Ueda, N., Yamada, T.: Graph Clustering based on the Nonparametric Bayes Model. IEICE Technical Report, vol. 107, no. 115, PRMU2007-41, pp. 81–86 (2007) (in Japanese)

    Google Scholar 

  7. Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. USA 99, 7821–7826 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  8. Roweis, S.: Data for MATLAB hackersNIPS Conference Papers., http://www.cs.toronto.edu/~roweis/data.html

Download references

Author information

Authors and Affiliations

  1. Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya, 466-8555, Japan

    Naoyuki Harada, Yuta Ishikawa & Ichiro Takeuchi

  2. Chubu University, 1200 Matsumoto-cho, Kasugai-shi, Aichi, 486-8507, Japan

    Ryohei Nakano

Authors
  1. Naoyuki Harada
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuta Ishikawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ichiro Takeuchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ryohei Nakano
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Electronic Engineering, City University of Hong Kong, Hong Kong,  

    Chi Sing Leung

  2. School of Electrical Engineering and Computer Science, Kyungpook National University, 1370 Sankyuk-Dong, Puk-Gu, 702-701, Taegu, Korea

    Minho Lee

  3. School of Information Technology, King Mongkut’s University of Technology Thonburi, 126 Pracha-U-Thit Rd., Bangmod, Thungkru, 10140, Bangkok, Thailand

    Jonathan H. Chan

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Harada, N., Ishikawa, Y., Takeuchi, I., Nakano, R. (2009). A Bayesian Graph Clustering Approach Using the Prior Based on Degree Distribution. In: Leung, C.S., Lee, M., Chan, J.H. (eds) Neural Information Processing. ICONIP 2009. Lecture Notes in Computer Science, vol 5863. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10677-4_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-10677-4_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10676-7

  • Online ISBN: 978-3-642-10677-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation