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. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Xiao, B., & Hancock, E. R. (2005). Graph clustering using heat content invariants. In Lecture Notes in Computer Science (Vol. 3523, pp. 123–130). Springer Verlag. https://doi.org/10.1007/11492542_16
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