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Social Coordinates: A Scalable Embedding Framework for Online Social Networks
UC Davis, USA.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.ORCID iD: 0000-0003-3219-9598
UC Davis, USA.
2017 (English)In: Proceedings of the 2017 International Conference on Machine Learning and Soft Computing, ACM Digital Library, 2017, 191-196 p.Conference paper, (Refereed)
Abstract [en]

We present a scalable framework to embed nodes of a large social network into an Euclidean space such that the proximity between embedded points reflects the similarity between the corresponding graph nodes. Axes of the embedded space are chosen to maximize data variance so that the dimension of the embedded space is a parameter to regulate noise in data. Using recommender system as a benchmark, empirical results show that similarity derived from the embedded coordinates outperforms similarity obtained from the original graph-based measures.

Place, publisher, year, edition, pages
ACM Digital Library, 2017. 191-196 p.
Keyword [en]
Embedding; Graph kernels; Online social networks
National Category
Computer Science
Identifiers
URN: urn:nbn:se:bth-14116DOI: 10.1145/3036290.3036298ISBN: 978-1-4503-4828-7 (electronic)OAI: oai:DiVA.org:bth-14116DiVA: diva2:1089324
Conference
2017 International Conference on Machine Learning and Soft Computing, ICMLSC, Ho Chi Minh City
Available from: 2017-04-19 Created: 2017-04-19 Last updated: 2017-05-26Bibliographically approved

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fulltext(21045 kB)101 downloads
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Erlandsson, Fredrik
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • 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