Abstract
In this paper, we investigate the heat kernel embedding as a route to computing geometric characterisations of graphs. The reason for turning to the heat kernel is that it encapsulates information concerning the distribution of path lengths and hence node affinities on the graph. The heat kernel of the graph is found by exponentiating the Laplacian eigensystem over time. The matrix of embedding co-ordinates for the nodes of the graph is obtained by performing a Young-Householder decomposition on the heat kernel. Once the embedding of its nodes is to hand we proceed to characterise a graph in a geometric manner. To obtain this characterisation, we focus on the edges of the graph under the embedding. Here we use the difference between geodesic and Euclidean distances between nodes to associate a sectional curvature with edges. Once the section curvatures are to hand then the Gauss-Bonnet theorem allows us to compute Gaussian curvatures at nodes on the graph. We explore how the attributes furnished by this analysis can be used to match and cluster graphs.
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Atkins, J.E., Boman, E.G., Hendrickson, B.: A spectral algorithm for seriation and the consecutive ones problem. SIAM J. Comput. 28(1), 297–310 (1998)
Barlow, M.T.: Diffusions on fractals. Lecture Notes Math., vol. 1690, pp. 1–121. Springer, Heidelberg (1998)
Belkin, M., Niyogi, P.: Laplacian eigenmaps and spectral techniques for embedding and clustering. In: Advances in Neural Information Processing Systems, vol. 14 (2002)
Chung, F.R.K.: Spectral graph theory. CBMS 92 (1997)
Cox, T., Cox, M.: Multidimensional Scaling. Chapman-Hall, Boca Raton (1994)
de Verdi‘ere, Y.C.: Spectres de graphes. Societe Mathematique De France (1998)
Dubuisson, M., Jain, A.: A modified Hausdorff distance for object matching, pp. 566–568 (1994)
Gilkey, P.B.: Invariance theory, heat equation, and the index theorem. Mathematics Lecture Series (1984)
Grigor’yan, A.: Heat kernels on manifolds, graphs and fractals. European Congress of Mathematics I, 393–406 (2001)
Heut, B., Hancock, E.R.: Relational object recognition from large structural libraries. Pattern Recognition 32, 1895–1915 (2002)
Huttenlocher, D., Klanderman, G., Rucklidge, W.: Comparing images using the Hausdorff distance. IEEE. Trans. Pattern Anal. Mach. Intell. 15, 850–863 (1993)
Lebanon, G., Lafferty, J.D.: Hyperplane margin classifiers on the multinomial manifold. In: ICML (2004)
Lindman, H., Caelli, T.: Constant curvature Riemannian scaling. Journal of Mathematical Psychology 17, 89–109 (1978)
Linial, N., London, E., Rabinovich, Y.: The geometry of graphs and some of its algorithmic applications. Combinatorica 15, 215–245 (1995)
Luo, B., Hancock, E.R.: Structural graph matching using the EM algorithm and singular value decomposition. IEEE Trans. Pattern Anal. Mach. Intell. 23(10), 1120–1136 (2001)
Luo, B., Wilson, R.C., Hancock, E.R.: Spectral embedding of graphs. Pattern Recogintion 36, 2213–2230 (2003)
Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290 (5500), 2323–2326 (2000)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE PAMI 22, 888–905 (2000)
Shokoufandeh, A., Dickinson, S.J., Siddiqi, K., Zucker, S.W.: Indexing using a spectral encoding of topological structure. In: CVPR, pp. 2491–2497 (1999)
Smola, E.J., Kondor, R.: Kernels and regularization on graphs (2004)
Spivak, M.: A Comprehensive Introduction to Differential Geometry, 2nd edn., vol. 1-5. Publish or Parish, Houston (1979)
Stillwell, J.: Mathematics and its History. Springer, New York (1974)
Tenenbaum, J.B., de Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290, 2319 (2000)
Umeyama, S.: An eigendecomposition approach to weighted graph matching problems. IEEE Trans. Patt. Anal. Mach. Intell. 10, 695–703 (1988)
Xiao, B., Hancock, E.R.: Trace formula analysis of graphs. In: Yeung, D.-Y., Kwok, J.T., Fred, A., Roli, F., de Ridder, D. (eds.) SSPR 2006 and SPR 2006. LNCS, vol. 4109, pp. 306–313. Springer, Heidelberg (2006)
Yau, S.T., Schoen, R.M.: Differential Geometry. Science Publication Co. (1988) (in Chinese)
Zhou, D., Schölkopf, B.: A regularization framework for learning from graph data. In: ICML Workshop on Statistical Relational Learning and Its Connections to Other Fields, pp. 132–137 (2004)
Zhu, X., Kandola, J.S., Ghahramani, Z., Lafferty, J.D.: Nonparametric transforms of graph kernels for semi-supervised learning. In: NIPS (2004)
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El-Ghawalby, H., Hancock, E.R. (2009). Geometric Characterizations of Graphs Using Heat Kernel Embeddings. In: Hancock, E.R., Martin, R.R., Sabin, M.A. (eds) Mathematics of Surfaces XIII. Mathematics of Surfaces 2009. Lecture Notes in Computer Science, vol 5654. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03596-8_8
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DOI: https://doi.org/10.1007/978-3-642-03596-8_8
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