Graph diffusion distance: a difference measure for weighted graphs based on the graph Laplacian exponential kernel

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Publication Type pre-print
School or College <blank>
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Creator Gur, Yaniv
Other Author Hammond, David K.; Johnson, Chris R.
Title Graph diffusion distance: a difference measure for weighted graphs based on the graph Laplacian exponential kernel
Date 2013-01-01
Description We propose a novel difference metric, called the graph diffusion distance (GDD), for quantifying the difference between two weighted graphs with the same number of vertices. Our approach is based on measuring the average similarity of heat diffusion on each graph. We compute the graph Laplacian exponential kernel matrices, corresponding to repeatedly solving the heat diffusion problem with initial conditions localized to single vertices. The GDD is then given by the Frobenius norm of the difference of the kernels, at the diffusion time yielding the maximum difference. We study properties of the proposed distance on both synthetic examples, and on real-data graphs representing human anatomical brain connectivity.
Type Text
Publisher Institute of Electrical and Electronics Engineers (IEEE)
First Page 419
Last Page 422
Language eng
Bibliographic Citation Hammond, D. K., Gur, Y., & Johnson, C. R. (2014). Graph diffusion distance: a difference measure for weighted graphs based on the graph Laplacian exponential kernel. 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings, 419-22.
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Identifier uspace,18613
ARK ark:/87278/s67119j9
Setname ir_uspace
ID 712514
Reference URL https://collections.lib.utah.edu/ark:/87278/s67119j9
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