Rossi, Luca, Torsello, Andrea and Hancock, Edwin R. orcid.org/0000-0003-4496-2028 (2014) Node centrality for continuous-time quantum walks. 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. 103-112.
Abstract
The study of complex networks has recently attracted increasing interest because of the large variety of systems that can be modeled using graphs. A fundamental operation in the analysis of complex networks is that of measuring the centrality of a vertex. In this paper, we propose to measure vertex centrality using a continuous-time quantum walk. More specifically, we relate the importance of a vertex to the influence that its initial phase has on the interference patterns that emerge during the quantum walk evolution. To this end, we make use of the quantum Jensen-Shannon divergence between two suitably defined quantum states. We investigate how the importance varies as we change the initial state of the walk and the Hamiltonian of the system. We find that, for a suitable combination of the two, the importance of a vertex is almost linearly correlated with its degree. Finally, we evaluate the proposed measure on two commonly used networks.
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 accepted for publication in Lecture Notes in Computer Science. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Complex Network,Quantum Jensen-Shannon Divergence,Quantum Walk,Vertex Centrality |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) The University of York |
Depositing User: | Pure (York) |
Date Deposited: | 15 Dec 2015 14:00 |
Last Modified: | 21 Feb 2025 00:10 |
Published Version: | https://doi.org/10.1007/978-3-662-44415-3_11 |
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_11 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:85365 |
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