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Effective and efficient location influence mining in location-based social networks

Author
Aamir, M.; Kumar, R.; Calders, T.; Bach, T.
Type of activity
Journal article
Journal
Knowledge and information systems
Date of publication
2018-07-07
First page
1
Last page
36
DOI
https://doi.org/10.1007/s10115-018-1240-8 Open in new window
URL
https://link.springer.com/article/10.1007/s10115-018-1240-8 Open in new window
Abstract
Location-based social networks (LBSN) are social networks complemented with location data such as geo-tagged activity data of its users. In this paper, we study how users of an LBSN are navigating between locations and based on this information we select the most influential locations. In contrast to existing works on influence maximization, we are not per se interested in selecting the users with the largest set of friends or the set of locations visited by the most users; instead, we introduce...
Keywords
Location-based social networks Location influence Influence maximization Geographical spread

Participants

  • Aamir, Muhammad  (author)
  • Kumar, Rohit  (author)
  • Calders, Toon  (author)
  • Bach Pedersen, Torben  (author)