STUN: Spatio-temporal uncertain (social) networks

Chanhyun Kang*, Andrea Pugliese, John Grant, V. S. Subrahmanian

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

STUN is an extension of social networks in which the edges are characterized by spatio-temporal annotations, as well as uncertainty allowing us to express not only relationships between vertices, but when and where these relationships were true, and how certain we are that the relationships hold. We propose a STUN query language that consists of subgraphs with spatiotemporal constraints and uncertainty requirements. We then develop an index structure to store STUN graphs, together with an algorithm to answer such queries. We describe experiments with a real-world YouTube social network data set and show that our algorithm performs well on graphs with over a million edges.

Original languageEnglish (US)
Title of host publicationProceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012
Pages543-550
Number of pages8
DOIs
StatePublished - 2012
Externally publishedYes
Event2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012 - Istanbul, Turkey
Duration: Aug 26 2012Aug 29 2012

Publication series

NameProceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012

Other

Other2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012
Country/TerritoryTurkey
CityIstanbul
Period8/26/128/29/12

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software

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