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

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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 a notion of location influence that captures the ability of a set of locations to reach out geographically by utilizing their visitors as message carriers. We further capture the influence of these visitors on their friends in LBSNs and utilize them to predict the potential future location influence more accurately. We provide exact online algorithms and more memory efficient but approximate variants based on the HyperLogLog and the modified HyperLogLog sketch to maintain a data structure called Influence Oracle that allows to efficiently find a top-k set of influential locations. Experiments show that our new location influence notion favors diverse sets of locations with a large geographical spread and that our algorithms are efficient, scalable and allow to capture future location influence.

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Fig. 1

Reproduced with permission from Saleem et al. [26]

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Reproduced with permission from Saleem et al. [26]

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Reproduced with permission from Saleem et al. [26]

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Notes

  1. Code of the algorithms are given at: https://github.com/rohit13k/LBSNAnalysis.

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Acknowledgements

This research has been funded in part by the European Commission through the Erasmus Mundus Joint Doctorate “Information Technologies for Business Intelligence-Doctoral College” (IT4BI-DC). Rohit Kumar is supported by Fonds de la Recherche Scientifique-FNRS under Grant No. T.0183.14 PDR.

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Authors and Affiliations

  1. Aalborg University, Aalborg, Denmark

    Muhammad Aamir Saleem & Torben Bach Pedersen

  2. Universite Libre de Bruxelles, Brussels, Belgium

    Muhammad Aamir Saleem, Rohit Kumar & Toon Calders

  3. Universitat Politecnica de Catalunya, Barcelona, Spain

    Rohit Kumar

  4. University of Antwerp, Antwerp, Belgium

    Toon Calders

Authors
  1. Muhammad Aamir Saleem
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  2. Rohit Kumar
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  3. Toon Calders
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  4. Torben Bach Pedersen
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Corresponding author

Correspondence to Muhammad Aamir Saleem.

Additional information

This paper is a significant extension of the conference paper [26].

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Saleem, M.A., Kumar, R., Calders, T. et al. Effective and efficient location influence mining in location-based social networks. Knowl Inf Syst 61, 327–362 (2019). https://doi.org/10.1007/s10115-018-1240-8

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  • DOI: https://doi.org/10.1007/s10115-018-1240-8

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