Inferring the Social-Connectedness of Locations from Mobility Data

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10540)


An often discriminating feature of a location is its social character or how well its visitors know each other. In this paper, we address the question of how we can infer the social contentedness of a location by observing the presence of mobile entities in it. We study a large number of mobility features that can be extracted from visits to a location. We use these features for predicting the social tie strengths of the device owners present in the location at a given moment in time, and output an aggregate score of social connectedness for that location. We evaluate this method by testing it on a real-world dataset. Using a synthetically modified version of this dataset, we further evaluate its robustness against factors that normally degrade the quality of such ubiquitously collected data (e.g. noise, sampling frequency). In each case, we found that the accuracy of the proposed method highly outperforms that of a state-of-the-art baseline methodology.


Spatial profiling Link prediction Mobility data mining Wi-Fi scanning Mobility modeling 


  1. 1.
    Seeman, T.E.: Social ties and health: the benefits of social integration. Ann. Epidemiol. 6(5), 442–451 (1996)CrossRefGoogle Scholar
  2. 2.
    Kawachi, I., Berkman, L.F.: Social ties and mental health. J. Urban Health 78(3), 458–467 (2001)CrossRefGoogle Scholar
  3. 3.
    Jylhä, M., Aro, S.: Social ties and survival among the elderly in tampere, finland. Int. J. Epidemiol. 18(1), 158–164 (1989)CrossRefGoogle Scholar
  4. 4.
    Baratchi, M., Heijenk, G., van Steen, M.: Spaceprint: a mobility-based fingerprinting scheme for public spaces. arXiv preprint arXiv:1703.09962 (2017)
  5. 5.
    Petre, A.C., Chilipirea, C., Baratchi, M., Dobre, C., van Steen, M.: WiFi tracking of pedestrian behavior. In: Smart Sensors Networks: Communication Technologies and Intelligent Applications. Elsevier (2017)Google Scholar
  6. 6.
    Cunche, M., Kaafar, M.A., Boreli, R.: Linking wireless devices using information contained in Wi-Fi probe requests. Pervasive Mob. Comput. 11, 56–69 (2014)CrossRefGoogle Scholar
  7. 7.
    Barbera, M.V., Epasto, A., Mei, A., Perta, V.C.: Signals from the crowd: uncovering social relationships through smartphone probes. In: Proceedings of the 2013 Conference on Internet Measurement Conference, pp. 265–276. ACM (2013)Google Scholar
  8. 8.
    McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: homophily in social networks. Ann. Rev. Sociol. 27, 415–444 (2001)CrossRefGoogle Scholar
  9. 9.
    Mashhadi, A., Vanderhulst, G., Acer, U.G., Kawsar, F.: An autonomous reputation framework for physical locations based on WiFi signals. In: Proceedings of the 2nd Workshop on Workshop on Physical Analytics, pp. 43–46. ACM (2015)Google Scholar
  10. 10.
    Cheng, N., Mohapatra, P., Cunche, M., Kaafar, M.A., Boreli, R.: Inferring user relationship from hidden information in WLANs. In: 2012–2012 IEEE Military Communications Conference on MILCOM, pp. 1–6. IEEE (2012)Google Scholar
  11. 11.
    Scellato, S., Noulas, A., Mascolo, C.: Exploiting place features in link prediction on location-based social networks. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1046–1054. ACM (2011)Google Scholar
  12. 12.
    Eagle, N., Pentland, A.S., Lazer, D.: Inferring friendship network structure by using mobile phone data. Proc. Natl. Acad. Sci. 106(36), 15274–15278 (2009)CrossRefGoogle Scholar
  13. 13.
    Baratchi, M., Meratnia, N., Havinga, P.J.M.: On the use of mobility data for discovery and description of social ties. In: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013, NY, USA, pp. 1229–1236 (2013).
  14. 14.
    Di Luzio, A., Mei, A., Stefa, J.: Mind your probes: De-anonymization of large crowds through smartphone WiFi probe requests. In: IEEE The 35th Annual IEEE International Conference on Computer Communications, INFOCOM 2016, pp. 1–9. IEEE (2016)Google Scholar
  15. 15.
    Blum, A.L., Langley, P.: Selection of relevant features and examples in machine learning. Artif. Intell. 97(1), 245–271 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Misra, B.: iOS8 MAC randomization analyzed! (2014). Accessed 21 Nov 2016

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.University of TwenteEnschedeThe Netherlands

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