Aziz, Furqan, Wilson, Richard C. orcid.org/0000-0001-7265-3033 and Hancock, Edwin R. orcid.org/0000-0003-4496-2028 (2014) Commute time for a gaussian wave packet on a graph. In: Structural, Syntactic, and Statistical Pattern Recognition:Joint IAPR International Workshop, S+SSPR 2014, Joensuu, Finland, August 20-22, 2014. Proceedings. Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition, S+SSPR 2014, 20-22 Aug 2014 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) . Springer , GBR , pp. 374-383.
Abstract
This paper presents a novel approach to quantifying the information flow on a graph. The proposed approach is based on the solution of a wave equation, which is defined using the edge-based Laplacian of the graph. The initial condition of the wave equation is a Gaussian wave packet on a single edge of the graph. To measure the information flow on the graph, we use the average return time of the Gaussian wave packet, referred to as the wave packet commute time. The advantage of using the edge-based Laplacian of a graph over its vertex-based counterpart is that it translates results from traditional analysis to graph theoretic domain in a more natural way. Therefore it can be useful in applications where distance and speed of propagation are important.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Springer Verlag 2014. This is an author produced version of a paper published in Lecture Notes in Computer Science. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Edge-based Laplacian,graph complexity,speed of propagation,wave commute time,wave equation |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 15 Dec 2015 14:05 |
Last Modified: | 16 Oct 2024 10:43 |
Published Version: | https://doi.org/10.1007/978-3-662-44415-3_38 |
Status: | Published |
Publisher: | Springer |
Series Name: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Identification Number: | 10.1007/978-3-662-44415-3_38 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:85367 |
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