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Link prediction in evolutionary graphs the case study of the CCIA network
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2016 (English)In: 19th International Conference of the Catalan Association for Artificial Intelligence, CCIA 2016, IOS Press, 2016, 187-196 p.Conference paper, Published paper (Refereed)
Abstract [en]

Studying the prediction of new links in evolutionary networks is a captivating question that has received the interest of different disciplines. Link prediction allows to extract missing information and evaluate network dynamics. Some algorithms that tackle this problem with good performances are based on the sociability index, a measure of node interactions over time. In this paper, we present a case study of this predictor in the evolutionary graph that represents the CCIA co-authorship network from 2005 to 2015. Moreover, we present a generalized version of this sociability index, that takes into account the time in which such interactions occur. We show that this new index outperforms existing predictors. Finally, we use it in order to predict new co-authorships for CCIA 2016.

Place, publisher, year, edition, pages
IOS Press, 2016. 187-196 p.
Keyword [en]
CCIA coauthorship network, Evolutionary networks, Link prediction, Artificial intelligence, Publishing, Co-authorship networks, Co-authorships, Evolutionary network, Missing information, Network dynamics, Forecasting
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-195444DOI: 10.3233/978-1-61499-696-5-187ISI: 000390305800025Scopus ID: 2-s2.0-84988530115ISBN: 9781614996958 (print)OAI: oai:DiVA.org:kth-195444DiVA: diva2:1045376
Conference
19 October 2016 through 21 October 2016
Note

QC 20161109

Available from: 2016-11-09 Created: 2016-11-03 Last updated: 2017-03-01Bibliographically approved

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Giraldez-Cru, J.
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CiteExportLink to record
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Citation style
  • apa
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  • ieee
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More styles
Language
  • de-DE
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  • fi-FI
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  • nn-NB
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  • Other locale
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Output format
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  • asciidoc
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