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

  • Muhammad Aamir SaleemEmail author
  • Rohit Kumar
  • Toon Calders
  • Torben Bach Pedersen
Regular Paper
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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.

Keywords

Location-based social networks Location influence Influence maximization Geographical spread 

Notes

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.

References

  1. 1.
    AlDwyish A, Tanin E, Karunasekera S (2015) Location-based social networking for obtaining personalised driving advice. In: SIGSPATIALGoogle Scholar
  2. 2.
    Bouros P, Sacharidis D, Bikakis N (2014) Regionally influential users in location-aware social networks. In: SIGSPATIALGoogle Scholar
  3. 3.
    Braam RR, Moed HF, Van Raan AF (1988) Mapping of science: critical elaboration and new approaches, a case study in agricultural biochemistry. In: InformetricsGoogle Scholar
  4. 4.
    Chen W, Wang Y, Yang S (2009) Efficient influence maximization in social networks. In: KDDGoogle Scholar
  5. 5.
    Cho E, Myers SA, Leskovec J (2011) Friendship and mobility: user movement in location-based social networks. In: KDDGoogle Scholar
  6. 6.
    Cohen E, Delling D, Pajor T, Werneck RF (2014) Sketch-based influence maximization and computation: scaling up with guarantees. In: CIKMGoogle Scholar
  7. 7.
    Doan T-N, Chua FCT, Lim E-P (2015) Mining business competitiveness from user visitation data. In: SBPGoogle Scholar
  8. 8.
    Domingos P, Richardson M (2001) Mining the network value of customers. In: KDDGoogle Scholar
  9. 9.
    Du N, Song L, Gomez-Rodriguez M, Zha H (2013) Scalable influence estimation in continuous-time diffusion networks. In: NIPSGoogle Scholar
  10. 10.
    Ferrari L, Rosi A, Mamei M, Zambonelli F (2011) Extracting urban patterns from location-based social networks. In: SIGSPATIALGoogle Scholar
  11. 11.
    Flajolet P, Fusy É, Gandouet O, Meunier F (2008) Hyperloglog: the analysis of a near-optimal cardinality estimation algorithm. In: DMTCSGoogle Scholar
  12. 12.
    Gao H, Tang J, Liu H (2012) Exploring social-historical ties on location-based social networks. In: AAAIGoogle Scholar
  13. 13.
    Gomez-Rodriguez M, Schölkopf B (2012) Influence maximization in continuous time diffusion networks. In: ICMLGoogle Scholar
  14. 14.
    Goyal A, Bonchi F, Lakshmanan LV (2008) Discovering leaders from community actions. In: CIKMGoogle Scholar
  15. 15.
    Goyal A, Bonchi F, Lakshmanan LV (2010) Learning influence probabilities in social networks. In: WSDMGoogle Scholar
  16. 16.
    Goyal A, Bonchi F, Lakshmanan LVS (2011) A data-based approach to social influence maximization. In: PVLDBGoogle Scholar
  17. 17.
    Hai NT (2015) A novel approach for location promotion on location-based social networks. In: RIVFGoogle Scholar
  18. 18.
    Kempe D, Kleinberg J, Tardos E (2003) Maximizing the spread of influence through a social network. In: KDDGoogle Scholar
  19. 19.
    Kumar R, Calders T (2017) Information propagation in interaction networks. In: EDBTGoogle Scholar
  20. 20.
    Li G, Chen S, Feng J, Tan K-l, Li W-s (2014) Efficient location-aware influence maximization. In: SIGMODGoogle Scholar
  21. 21.
    Liu Q, Deng M, Shi Y, Wang J (2012) A density-based spatial clustering algorithm considering both spatial proximity and attribute similarity. Comput Geosci 46:296–309CrossRefGoogle Scholar
  22. 22.
    LováSz L (2005) Review of the book by Alexander Schrijver: combinatorial optimization: polyhedra and efficiency. Oper Res Lett 33:437–440CrossRefGoogle Scholar
  23. 23.
    Mata FJ, Quesada A (2014) Web 2.0, social networks and e-commerce as marketing tools. J Theor Appl Electron Commer Res 9:56–69CrossRefGoogle Scholar
  24. 24.
    Richardson M, Domingos P (2002) Mining knowledge-sharing sites for viral marketing. In: KDDGoogle Scholar
  25. 25.
    Saleem MA, Costa FSd, Dolog P, Karras P, Pedersen TB, Calders T (2018) Predicting visitors using location-based social networks. In: MDMGoogle Scholar
  26. 26.
    Saleem MA, Kumar R, Calders T, Xie X, Pedersen TB (2017) Location influence in location-based social networks. In: WSDMGoogle Scholar
  27. 27.
    Saleem MA, Xie X, Pedersen TB (2016) Scalable processing of location-based social networking queries. In: MDMGoogle Scholar
  28. 28.
    Wang X, Zhang Y, Zhang W, Lin X (2016) Distance-aware influence maximization in geo-social network. In: ICDEGoogle Scholar
  29. 29.
    Wen Y-T, Lei P-R, Peng W-C, Zhou X-F (2014) Exploring social influence on location-based social networks. In: ICDMGoogle Scholar
  30. 30.
    Wu H, Cheng J, Huang S, Ke Y, Lu Y, Xu Y (2014) Path problems in temporal graphs. Proc VLDB Endow 7(9):721–732CrossRefGoogle Scholar
  31. 31.
    Wu H-H, Yeh M-Y (2013) Influential nodes in a one-wave diffusion model for location-based social networks. In: PAKDDGoogle Scholar
  32. 32.
    Zhang C, Shou L, Chen K, Chen G, Bei Y (2012) Evaluating geo-social influence in location-based social networks. In: CIKMGoogle Scholar
  33. 33.
    Zhou T, Cao J, Liu B, Xu S, Zhu Z, Luo J (2015) Location-based influence maximization in social networks. In: CIKMGoogle Scholar
  34. 34.
    Zhu W-Y, Peng W-C, Chen L-J, Zheng K, Zhou X (2015) Modeling user mobility for location promotion in location-based social networks. In: KDDGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  • Muhammad Aamir Saleem
    • 1
    • 2
    Email author
  • Rohit Kumar
    • 2
    • 3
  • Toon Calders
    • 2
    • 4
  • Torben Bach Pedersen
    • 1
  1. 1.Aalborg UniversityAalborgDenmark
  2. 2.Universite Libre de BruxellesBrusselsBelgium
  3. 3.Universitat Politecnica de CatalunyaBarcelonaSpain
  4. 4.University of AntwerpAntwerpBelgium

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