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Clustered Embedding of Massive Social Networks
Department of Computer Science, The University of Texas at Austin.
Linköping University, Department of Mathematics, Scientific Computing. Linköping University, The Institute of Technology. (Institute for Computational Engineering and Sciences, The University of Texas at Austin)ORCID iD: 0000-0002-1542-2690
Department of Computer Science, The University of Texas at Austin.
Department of Computer Science, The University of Texas at Austin.
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2012 (English)In: Proceedings of the 12th ACM SIGMETRICS/PERFORMANCE joint international conference on Measurement and Modeling of Computer Systems, Association for Computing Machinery (ACM), 2012, , 27 p.331-342 p.Conference paper, Published paper (Other academic)
Abstract [en]

The explosive growth of social networks has created numerous exciting research opportunities. A central concept in the analysis of social networks is a proximity measure, which captures the closeness or similarity between nodes in a social network. Despite much research on proximity measures,  there is a lack of techniques to eciently and accurately compute proximity measures for large-scale social networks. In this paper, we develop a novel dimensionality reduction technique, called clustered spectral graph embedding, to embed the graphs adjacency matrix into a much smaller matrix. The embedded matrix together with the embedding subspaces capture the essential clustering and spectral structure of the original graph and allows a wide range of analysis tasks to be performed in an ecient and accurate fashion. To evaluate our technique, we use three large real-world social  network datasets: Flickr, LiveJournal and MySpace, with up to 2 million nodes and 90 million links. Our results clearly demonstrate the accuracy, scalability and  exibility of our approach in the context of three importantsocial network analysis tasks: proximity estimation, missing link inference, and link prediction.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2012. , 27 p.331-342 p.
Series
ACM SIGMETRICS Performance Evaluation Review, ISSN 0163-5999 ; Vol. 40, Iss.1
National Category
Other Computer and Information Science
Identifiers
URN: urn:nbn:se:liu:diva-86053DOI: 10.1145/2254756.2254796ISBN: 978-1-4503-1097-0 (print)OAI: oai:DiVA.org:liu-86053DiVA: diva2:574698
Conference
the 12th ACM SIGMETRICS/PERFORMANCE joint international conference on Measurement and Modeling of Computer Systems, June 11-15, London, UK
Available from: 2012-12-06 Created: 2012-12-06 Last updated: 2014-03-27Bibliographically approved

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Savas, Berkant

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf