Skip to main content
SpringerLink
Log in

Discovering Interest Based Mobile Communities

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

Human mobility has attracted lots of attention in the domain of wireless communication technologies and behavioral sciences. Exploring and analyzing human movements that are changing over time, relating to user habits and depending on spacial aspect is a challenging problem. In this paper, we are interested in discovering communities of mobile users and studying how do communities provide accurate knowledge to analysis the different forms of human mobility. The basic idea of our work is to model the behaviour of users with strong social characteristics regarding the context of their location histories to explore a similar interest of people by mining their mobiles communities. The proposed analysis illustrates in what way a common interest of a group of individuals can create better understanding of human mobility. Realistic models based on these interest based communities can be the basis for applications as recommendation system or wireless networks management.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Aggarwal CC, Procopiuc C, Yu PS (2002) Finding localized associations in market basket data. IEEE Trans Knowl Data Eng 14(1):51–62

    Article  Google Scholar 

  2. Agrawal R, Imieliński T, Swami A Mining association rules between sets of items in large databases Acm sigmod record, vol 22. ACM, pp 207–216

  3. Barabàsi AL (2003) Linked: The new science of networks

  4. Baraldi AN, Enders CK (2010) An introduction to modern missing data analyses. J Sch Psychol 48(1):5–37

    Article  Google Scholar 

  5. Boccaletti S, Latora V, Moreno Y, Chavez M, Hwang D-U (2006) Complex networks: Structure and dynamics. Phys Rep 424(4):175–308

    Article  MathSciNet  Google Scholar 

  6. Cao H, Mamoulis H, Cheung DW (2005) Mining frequent spatio-temporal sequential patterns Fifth IEEE International Conference on Data Mining (ICDM’05). IEEE, pp 8–pp

  7. Chen L, Lv M, Chen G (2010) A system for destination and future route prediction based on trajectory mining

  8. Ester M, Kriegel H-P, Sander J, Xu X et al A density-based algorithm for discovering clusters in large spatial databases with noise Kdd, vol 96, pp 226–231

  9. Fang H, Hsu W-J, Rudolph L (2009) Mining user position log for construction of personalized activity map International Conference on Advanced Data Mining and Applications. Springer, pp 444–452

  10. Flake GW, Lawrence S, Giles CL, Coetzee FM (2002) Self-organization and identification of web communities. Computer 35(3):66–70

    Article  Google Scholar 

  11. Fortunato S Community detection in graphs 486(3):75–174

  12. Girvan M, Newman MEJ Community structure in social and biological networks 99(12):7821–7826

  13. Gonzalez MC, Hidalgo CA, Barabasi A-L Understanding individual human mobility patterns 453(7196):779–782

  14. Guimera R, Nunes Amaral LA Functional cartography of complex metabolic networks 433(7028):895–900

  15. Han J, Pei J, Yin Y, Mao R Mining frequent patterns without candidate generation: A frequent-pattern tree approach 8(1):53– 87

  16. Krause AE, Frank KA, Mason DM, Ulanowicz RE, Taylor WW (2003) Compartments revealed in food-web structure. Nature 426(6964):282–285

    Article  Google Scholar 

  17. Li Q, Zheng Y, Xie X, Chen Y, Liu W, Ma W-Y (2008) Mining user similarity based on location history Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems, vol 34. ACM

  18. Lusseau D, Newman MEJ (2004) Identifying the role that animals play in their social networks. Proc R Soc Lond B Biol Sci 271(Suppl 6):S477–S481

    Article  Google Scholar 

  19. Nanni M, Pedreschi D (2006) Time-focused clustering of trajectories of moving objects. J Intell Inf Syst 27(3):267–289

    Article  Google Scholar 

  20. Newman M Networks: an introduction. Oxford university press

  21. Newman MEJ (2003) Mixing patterns in networks, physical review E. 67:026126

  22. Newmanm MEJ, Girvan M (2004) Finding and evaluating community structure in networks 69(2):026113

  23. Nexus MB Small Worlds and the Groundbreaking Science of Networks. Norton Publishing

  24. Palla G, Derényi I, Farkas I, Vicsek T (2005) Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7043):814–818

    Article  Google Scholar 

  25. Papandrea M, Jahromi KK, Zignani M, Gaito S, Giordano S, Rossi GP (2016) On the properties of human mobility. Comput Commun 87:19–36

    Article  Google Scholar 

  26. Papandrea M, Zignani M, Gaito S, Giordano S, Rossi GP (2013) How many places do you visit a day? 2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops). IEEE, pp 218–223

  27. Pimm SL (1979) The structure of food webs. Theor Popul Biol 16(2):144–158

    Article  Google Scholar 

  28. Radicchi F, Castellano C, Cecconi F, Loreto V, Parisi D Defining and identifying communities in networks 101(9):2658– 2663

  29. Rhee I, Shin M, Hong S, Lee K, Kim S, Chong S (2009) Crawdad data set ncsu/mobility models

  30. Song C, Qu Z, Blumm N, Barabási A-L (2010) Limits of predictability in human mobility. Science 327(5968):1018–1021

    Article  MathSciNet  MATH  Google Scholar 

  31. Sorensen C, Mathiassen L, Kakihara M Mobile services: Functional diversity and overload

  32. Vincenty T (1975) Direct and inverse solutions of geodesics on the ellipsoid with application of nested equations. Surv Rev 23(176):88–93

    Article  Google Scholar 

  33. Zaki MJ (2000) Scalable algorithms for association mining. IEEE Trans Knowl Data Eng 12(3):372–390

    Article  Google Scholar 

  34. Zheng Y GeoLife GPS trajectories

  35. Zheng Y Location-based social networks: Users Computing with spatial trajectories. Springer, pp 243–276

  36. Zheng Y, Li Q, Chen Y, Xie X, Ma W-Y Understanding mobility based on GPS data Proceedings of the 10th international conference on Ubiquitous computing. ACM, pp 312–321

  37. Zheng Y, Xie X (2011) Learning travel recommendations from user-generated GPS traces. ACM Trans Intell Syst Technol (TIST) 2(1):2

    Google Scholar 

  38. Zheng Y, Xie X, Ma W-Y (2010) GeoLife: A collaborative social networking service among user, location and trajectory. IEEE Data Eng Bull, 33(2):3239

  39. Zheng Y, Zhang L, Xie X, Ma W-Y Mining interesting locations and travel sequences from GPS trajectories Proceedings of the 18th international conference on World wide web. ACM, pp 791–800

  40. Zhou H (2003) Distance, dissimilarity index, and network community structure. Phys Rev E 67(6):061901

    Article  Google Scholar 

  41. Zimmermann M, Kirste T, Spiliopoulou M Finding stops in error-prone trajectories of moving objects with time-based clustering Intelligent interactive assistance and mobile multimedia computing. Springer, pp 275–286

Download references

Author information

Authors and Affiliations

  1. Networks and Distrubuted System Laboratory, Faculty of Sciences, University of Setif 1, Setif, Algeria

    Ahlem Drif

  2. Computer Science Department, University of Bejaia, Bejaia, Algeria

    Abdellah Boukerram

  3. Laboratory of Intelligent Systems, University of Setif 1, Setif, Algeria

    Yacine Slimani

  4. Networking Lab, ISIN-DTI, University of Applied Sciences of Southern Switzerland - SUPSI, Manno, Switzerland

    Silvia Giordano

Authors
  1. Ahlem Drif
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abdellah Boukerram
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yacine Slimani
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Silvia Giordano
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ahlem Drif.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Drif, A., Boukerram, A., Slimani, Y. et al. Discovering Interest Based Mobile Communities. Mobile Netw Appl 22, 344–355 (2017). https://doi.org/10.1007/s11036-017-0811-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-017-0811-3

Keywords

Search

Navigation