STUN: Spatio-Temporal Uncertain (Social) Networks

  • Authors:

  • Chanhyun Kang;Andrea Pugliese;John Grant;V. S. Subrahmanian

  • Affiliations:

  • -;-;-;-

  • Venue:
  • ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
  • Year:
  • 2012

Quantified Score

Hi-index 0.00

Visualization

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.