• DocumentCode
    3157563
  • Title

    STUN: Spatio-Temporal Uncertain (Social) Networks

  • Author

    Chanhyun Kang ; Pugliese, Andrea ; Grant, James ; Subrahmanian, V.S.

  • Author_Institution
    Comput. Sci. Dept., Univ. of Maryland, College Park, MD, USA
  • fYear
    2012
  • fDate
    26-29 Aug. 2012
  • Firstpage
    543
  • Lastpage
    550
  • 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 sub graphs with spatio-temporal 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.
  • Keywords
    network theory (graphs); query languages; question answering (information retrieval); social networking (online); spatiotemporal phenomena; uncertainty handling; STUN; YouTube; index structure; query language; question answering system; social network; spatiotemporal constraint; spatiotemporal uncertain network; subgraph; uncertainty requirement; Educational institutions; Electronic mail; Facebook; Indexes; Knowledge based systems; Uncertainty;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advances in Social Networks Analysis and Mining (ASONAM), 2012 IEEE/ACM International Conference on
  • Conference_Location
    Istanbul
  • Print_ISBN
    978-1-4673-2497-7
  • Type

    conf

  • DOI
    10.1109/ASONAM.2012.93
  • Filename
    6425711