Personal and Ubiquitous Computing

, Volume 17, Issue 3, pp 413–431 | Cite as

Human interaction discovery in smartphone proximity networks

  • Trinh Minh Tri DoEmail author
  • Daniel Gatica-Perez
Original Article


Since humans are fundamentally social beings and interact frequently with others in their daily life, understanding social context is of primary importance in building context-aware applications. In this paper, using smartphone Bluetooth as a proximity sensor to create social networks, we present a probabilistic approach to mine human interaction types in real life. Our analysis is conducted on Bluetooth data continuously sensed with smartphones for over one year from 40 individuals who are professionally or personally related. The results show that the model can automatically discover a variety of social contexts. We objectively validated our model by studying its predictive and retrieval performance.


Social Network Analysis Time Slice Interaction Type Retrieval Performance Temporal Context 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was funded by Nokia Research Center Lausanne (NRC) through the LS-CONTEXT project.


  1. 1.
    Airoldi EM, Blei DM, Fienberg SE, Xing EP (2008) Mixed membership stochastic blockmodels. J Mach Learn Res 9:1981–2014zbMATHGoogle Scholar
  2. 2.
    Ashbrook D, Starner T (2003) Using gps to learn significant locations and predict movement across multiple users. Pers Ubiquitous Comput 7:275–286CrossRefGoogle Scholar
  3. 3.
    Banerjee N, Agarwal S, Bahl V, Chandra R, Wolman A, Corner MD (2010) Virtual Compass: relative positioning to sense mobile social interactions. In: Proc. Pervasive Computing, pp 1–21Google Scholar
  4. 4.
    Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3:993–1022zbMATHGoogle Scholar
  5. 5.
    Clauset A, Eagle N (2007) Persistence and periodicity in a dynamic proximity network. In: DIMACS workshop on computational methods for dynamic interaction networksGoogle Scholar
  6. 6.
    Dey AK (2001) Understanding and using context. Pers Ubiquitous Comput 5:4–7CrossRefGoogle Scholar
  7. 7.
    Do TMT, Gatica-Perez D (2011) Contextual grouping: discovering real-life interaction types from longitudinal bluetooth data. In: 12th international conference on mobile data managementGoogle Scholar
  8. 8.
    Do TMT, Gatica-Perez D (2011) Groupus: smartphone proximity data and human interaction type mining. In: 15th annual international symposium on wearable computersGoogle Scholar
  9. 9.
    DuBois C, Smyth P (2010) Modeling relational events via latent classes. In: Proceedings of KDD, pp 803–812Google Scholar
  10. 10.
    Eagle N, Pentland AS, Lazer D (2009) Inferring social network structure using mobile phone data. PNAS 106(36):15274–15278Google Scholar
  11. 11.
    Eagle N (Sandy), Pentland A (2006) Reality mining: sensing complex social systems. Pers Ubiquitous Comput 10(4):255–268CrossRefGoogle Scholar
  12. 12.
    Farrahi K, Gatica-Perez D (2008) What did you do today? Discovering daily routines from large-scale mobile data. In: ACM multimedia, pp 849–852Google Scholar
  13. 13.
    Fu W, Song L, Xing EP (2009) Dynamic mixed membership blockmodel for evolving networks. In: Proceedings of ICML, pp 329–336Google Scholar
  14. 14.
    Gilks WR (1999) Markov chain Monte Carlo. In: Practice, Chapman and Hall/CRCGoogle Scholar
  15. 15.
    Gips J, Pentland A (2006) Mapping human networks. In: Proceedings of pervasive computing and communications. IEEE Computer Society, pp 159–168Google Scholar
  16. 16.
    Griffiths TL, Steyvers M (2004) Finding scientific topics. PNAS 101(Suppl 1):5228–5235CrossRefGoogle Scholar
  17. 17.
    Hightower J, Consolvo S, Lamarca A, Smith I, Hughes J (2005) Learning and recognizing the places we go. In: Proceedings of UbiComp, pp 159–176Google Scholar
  18. 18.
    Hofmann T (1999) Probabilistic latent semantic indexing. In: SIGIR ’99: Proceedings of the 22nd annual international ACM SIGIR conference on research and development in information retrieval. ACM, New York, pp 50–57. doi: 10.1145/312624.312649
  19. 19.
    Huynh T, Fritz M, Schiele B (2008) Discovery of activity patterns using topic models. In: Proceedings of ubiquitous computing, ACM, pp 10–19Google Scholar
  20. 20.
    Jeon J, Lavrenko V, Manmatha R (2003) Automatic image annotation and retrieval using cross-media relevance models. In: Proceedings of SIGIR. ACM, New York, pp 119–126.
  21. 21.
    K, N, B, STA (2001) Estimation and prediction for stochastic blockstructures. JASA 96:1077–1087Google Scholar
  22. 22.
    Kiukkonen N, Blom J, Dousse O, Gatica-Perez D, Laurila J (2010) Towards rich mobile phone datasets: Lausanne data collection campaign. In: Proceedings of ICPS, BerlinGoogle Scholar
  23. 23.
    Krumm J, Horvitz E (2006) Predestination: inferring destinations from partial trajectories. In: Proceedings of ubiquitous computing, pp 243–260Google Scholar
  24. 24.
    Liao L, Fox D, Kautz H (2007) Extracting places and activities from gps traces using hierarchical conditional random fields. Int J Rob Res 26Google Scholar
  25. 25.
    Mardenfeld S, Boston D, Juan Pan S, Jones Q, Iamnitchi A, Cristian B (2010) Gdc: group discovery using co-location traces. In: SCAGoogle Scholar
  26. 26.
    McGovern A, Friedland L, Hay M, Gallagher B, Fast A, Neville J, Jensen D (2003) Exploiting relational structure to understand publication patterns in high-energy physics. SIGKDD Explor Newsl 5(2):165–172CrossRefGoogle Scholar
  27. 27.
    Mills TC (1990) Time series techniques for economists. Cambridge University Press, CambridgeGoogle Scholar
  28. 28.
    Minkov E, Cohen WW (2006) An email and meeting assistant using graph walks. In: CEASGoogle Scholar
  29. 29.
    Montoliu R, Gatica-Perez D (2010) Discovering human places of interest from multimodal mobile phone data. In: Proceedings of international conference on on mobile and ubiquitous multimediaGoogle Scholar
  30. 30.
    Olguin DO, Waber BN, Kim T, Mohan A, Ara K, Pentland A (2009) Sensible organizations: technology and methodology for automatically measuring organizational behavior. IEEE Trans Syst Man Cybern B Cybern 39:43–55Google Scholar
  31. 31.
    O’neill E, Kostakos V, Kindberg T, Schiek A, Penn A, Fraser D, Jones T (2006) Instrumenting the city: developing methods for observing and understanding the digital cityscape. In: Proceedings of UbiComp, pp 315–332Google Scholar
  32. 32.
    Patel SN, Kientz JA, Hayes GR, Bhat S, Abowd GD (2006) Farther than you may think: An empirical investigation of the proximity of users to their mobile phones. In: Proceedings of ubiquitous computing, P. Dourish. Springer, pp 123–140Google Scholar
  33. 33.
    Raento M, Oulasvirta A, Petit R, Toivonen H (2005) Contextphone: a prototyping platform for context-aware mobile applications. IEEE Perv Comput 4(2):51–59CrossRefGoogle Scholar
  34. 34.
    Sampson FS (1968) A novitiate in a period of change: an experimental and case study of social relationships. Ph.D. thesis, Cornell UniversityGoogle Scholar
  35. 35.
    Scott JP (1991) Social network analysis. SAGE, LondonGoogle Scholar
  36. 36.
    Terry M, Mynatt ED, Ryall K, Leigh D (2002) Social net: using patterns of physical proximity over time to infer shared interests. In: Proceedingo of CHI, pp 816–817.
  37. 37.
    Vetek A, Flanagan JA, Colley A, Keränen T (2009) Smartactions: Context-aware mobile phone shortcuts. In: INTERACT (1), pp 796–799Google Scholar
  38. 38.
    Wang X, Mohanty N, Mccallum A (2006) Group and topic discovery from relations and their attributes. In: Proceedings NIPS, pp 1449–1456Google Scholar
  39. 39.
    Wasserman S, Faust K (1994) Social Network analysis: methods and applications. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  40. 40.
    Wyatt D, Choudhury T, Kautz H (2007) Capturing spontaneous conversation and social dynamics: a private sensitive data collection. effort. In: Proceedings of ICASSPGoogle Scholar
  41. 41.
    Zheng B Jr, DCM, Lu X (2006) Identifying biological concepts from a protein-related corpus with a probabilistic topic model. BMC Bioinform 7:58Google Scholar
  42. 42.
    Ziebart BD, Maas AL, Dey AK, Bagnell JA (2008) Navigate like a cabbie: probabilistic reasoning from observed context-aware behavior. In: Proceedings of UbiComp ’08, pp 322–331Google Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.Idiap Research InstituteMartignySwitzerland
  2. 2.École Polytechnique Fédérale de LausanneLausanneSwitzerland

Personalised recommendations