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Three-hop distance estimation in social graphs.

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Published
  • Pascal Welke
  • Alexander Markowetz
  • Torsten Suel
  • Maria Christoforaki
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Publication date5/12/2016
Host publicationThree-hop distance estimation in social graphs.
PublisherIEEE
Pages1048-1055
Number of pages7
ISBN (electronic)9781467390057
ISBN (print)9781467390064
<mark>Original language</mark>Undefined/Unknown
Event2016 IEEE International Conference on Big Data (Big Data) - USA, Washington, DC
Duration: 5/12/20168/12/2016

Conference

Conference2016 IEEE International Conference on Big Data (Big Data)
CityWashington, DC
Period5/12/168/12/16

Conference

Conference2016 IEEE International Conference on Big Data (Big Data)
CityWashington, DC
Period5/12/168/12/16

Abstract

In this paper, we study a 3-hop approach to distance estimation that uses two intermediate landmarks, where each landmark only stores distances to vertices in its local neighborhood and to the other landmarks. We show how to suitably represent and compress the distance data stored for each landmark, for the 2-hop and 3-hop case. Overall, we find that 3-hop methods achieve modest but promising improvement in some cases, while being comparable or slightly worse than 2-hop methods in others. Furthermore, our light compression schemes improve the practical applicability of both the 2-hop and 3-hop methods.

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