Skip to main content

Graph Clustering Using Heat Content Invariants

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3523))

Abstract

In this paper, we investigate the use of invariants derived from the heat kernel as a means of clustering graphs. We turn to the heat-content, i.e. the sum of the elements of the heat kernel. The heat content can be expanded as a polynomial in time, and the co-efficients of the polynomial are known to be permutation invariants. We demonstrate how the polynomial co-efficients can be computed from the Laplacian eigensystem. Graph-clustering is performed by applying principal components analysis to vectors constructed from the polynomial co-efficients. We experiment with the resulting algorithm on the COIL database, where it is demonstrated to outperform the use of Laplacian eigenvalues.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Alexandrov, A.D., Zalgaller, V.A.: Intrinsic geometry of surfaces. Transl. Math. Monographs 15 (1967)

    Google Scholar 

  2. Atkins, J.E., Bowman, E.G., Hendrickson, B.: A spectral algorithm for seriation and the consecutive ones problem. SIAM J. Comput. 28, 297–310 (1998)

    Article  MATH  Google Scholar 

  3. de Verdiere, C.: Spectra of graphs. Math of France, 4 (1998)

    Google Scholar 

  4. Chung, F.R.K.: Spectral graph theory. American Mathematical Society (1997)

    Google Scholar 

  5. Hjaltason, G.R., Samet, H.: Properties of embedding methods for similarity searching in metric spaces. PAMI 25, 530–549 (2003)

    Google Scholar 

  6. Harris, C.G., Stephens, M.J.: A combined corner and edge detector. In: Fourth Alvey Vision Conference, pp. 147–151 (1994)

    Google Scholar 

  7. Linial, N., London, E., Rabinovich, Y.: The geometry of graphs and some its algorithmic application. Combinatorica 15, 215–245 (1995)

    Article  MATH  MathSciNet  Google Scholar 

  8. Mcdonald, P., Meyers, R.: Diffusions on graphs, poisson problems and spectral geometry. Transactions on Amercian Mathematical Society 354, 5111–5136 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  9. Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE PAMI 22, 888–905 (2000)

    Google Scholar 

  10. Yau, S.T., Schoen, R.M.: Differential geometry. Science Publication (1988)

    Google Scholar 

  11. Umeyama, S.: An eigen decomposition approach to weighted graph matching problems. IEEE PAMI 10, 695–703 (1988)

    MATH  Google Scholar 

  12. Weinberger, S.: Review of algebraic l-theory and topological manifolds by a.ranicki. BAMS 33, 93–99 (1996)

    Article  Google Scholar 

  13. Bai, X., Hancock, E.R.: Heat kernels, manifolds and graph embedding. In: Fred, A., Caelli, T.M., Duin, R.P.W., Campilho, A.C., de Ridder, D. (eds.) SSPR&SPR 2004. LNCS, vol. 3138, pp. 198–206. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Xiao, B., Hancock, E.R. (2005). Graph Clustering Using Heat Content Invariants. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds) Pattern Recognition and Image Analysis. IbPRIA 2005. Lecture Notes in Computer Science, vol 3523. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11492542_16

Download citation

  • DOI: https://doi.org/10.1007/11492542_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26154-4

  • Online ISBN: 978-3-540-32238-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics