Journal article
A self-consistent approach to measure preferential attachment in networks and its application to an inherent structure network
- Abstract:
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Preferential attachment is one possible way to obtain a scale-free network. We develop a self-consistent method to determine whether preferential attachment occurs during the growth of a network, and to extract the preferential attachment rule using time-dependent data. Model networks are grown with known preferential attachment rules to test the method, which is seen to be robust. The method is then applied to a scale-free inherent structure network, which represents the connections between ...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
-
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(Accepted manuscript, pdf, 261.3KB)
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- Publisher copy:
- 10.1016/j.physa.2006.11.007
Authors
Funding
Engineering and Physical Sciences Research Council
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Royal Society
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Bibliographic Details
- Publisher:
- Elsevier Publisher's website
- Journal:
- Physica A Journal website
- Volume:
- 377
- Issue:
- 1
- Pages:
- 351-362
- Publication date:
- 2005-12-06
- DOI:
- ISSN:
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0378-4371
- Source identifiers:
-
40206
Item Description
- Language:
- English
- Keywords:
- Pubs id:
-
pubs:40206
- UUID:
-
uuid:1b5a2c1b-25c7-4077-a828-1dd47785b6f1
- Local pid:
- pubs:40206
- Deposit date:
- 2012-12-19
Terms of use
- Copyright holder:
- Elsevier BV
- Copyright date:
- 2005
- Notes:
- Copyright 2006 Elsevier B.V. All rights reserved. NOTICE: this is the author's version of a work that was accepted for publication in Physica A: Statistical Mechanics and its Applications. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Physica A: Statistical Mechanics and its Applications, Volume 377, Issue 1, 1 April 2007, Pages 351-362, 10.1016/j.physa.2006.11.007
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