With the development of network services and location-based systems, many mobile applications begin to use users’ geographical location to provide better services. In terms of social networks, geographical location is actively shared by users. In some applications with recommendation services, before the geographical location recommendation is provided, the authors have to obtain user's permission. This kind of social network integrated with geographical location information is called location-based social networks (abbreviate for LBSNs). In the LBSN, each user has location information when he or she checked in hotels or feature spots. Based on this information, they can identify user's trajectory of movement behaviour and activity patterns. In general, if there is friendship between two users, their trajectories in reality are likely to be similar. In this study, according to user's geographical location information over a period of time, they explore whether there exists friendly relationship between two users based on trajectory similarity and the structure theory of graphs. In particular, they propose a new factor function and a factor graph model based on user's geographical location to predict the friendship between two users in the real LBSN.

References

1. 1)
  - [10]. Qiao, S., Han, N., Wang, J., et al: ‘Predicting long-term trajectories of connected vehicles via the prefix-projection technique’, IEEE Trans. Intell. Transp. Syst., 2018, 19, (7), pp. 2305–2315 (doi: 10.1109/TITS.2017.2750075).
2. 2)
  - [6]. Abbasi, O., Alesheikh, A., Sharif, M.: ‘Ranking the city: the role of location-based social media check-ins in collective human mobility prediction’, ISPRS. Int. J. Geoinf., 2017, 6, (5), p. 136 (doi: 10.3390/ijgi6050136).
3. 3)
  - [25]. Huo, Z., Meng, X., Hu, H., et al: ‘You can walk alone: trajectory privacy-preserving through significant stays protection’. Int. Conf. on Database Systems for Advanced Applications. (Springer), Busan, South Korea, 2012, pp. 351–366.
4. 4)
  - 1. Newman, M.E.J.: ‘The structure and function of complex networks’, SIAM Rev., 2003, 45, (2), pp. 167–256 (doi: 10.1137/S003614450342480).
5. 5)
  - [26]. Yin, H., Hu, Z., Zhou, X., et al: ‘Discovering interpretable geo-social communities for user behavior prediction’. 2016 IEEE 32nd Int. Conf. on Data Engineering (ICDE). (IEEE), Helsinki, Finland, 2016, pp. 942–953.
6. 6)
  - [21]. Scellato, S., Noulas, A., Mascolo, C.: ‘Exploiting place features in link prediction on location-based social networks’. Proc. of the 17th ACM SIGKDD int. Conf. on Knowledge discovery and data mining. (ACM), San Diego California, USA, 2011, pp. 1046–1054.
7. 7)
  - [20]. Crandall, D.J., Backstrom, L., Cosley, D., et al: ‘Inferring social ties from geographic coincidences’, Proc. Natl. Acad. Sci., 2010, 107, (52), pp. 22436–22441 (doi: 10.1073/pnas.1006155107).
8. 8)
  - [3]. Barrera, D., Kayacik, H.G., van Oorschot, P.C., et al: ‘A methodology for empirical analysis of permission-based security models and its application to android’. Proc. of the 17th ACM Conf. on Computer and Communications Security, New York, NY, USA, 2010, pp. 73–84.
9. 9)
  - [16]. Backstrom, L., Leskovec, J.: ‘Supervised random walks: predicting and recommending links in social networks’. Proc. of the Fourth ACM Int. Conf. On Web Search and Data Mining. WSDM'11, New York, NY, USA: Association for Computing Machinery), 2011, pp. 635–644.
10. 10)
  - [9]. Qiao, S., Shen, D., Wang, X., et al: ‘A self-adaptive parameter selection trajectory prediction approach via hidden Markov models’, IEEE Trans. Intell. Transp. Syst., 2015, 16, (1), pp. 284–296 (doi: 10.1109/TITS.2014.2331758).
11. 11)
  - [3]. Qiao, S., Tang, C., Jin, H., et al: ‘Putmode: prediction of uncertain trajectories in moving objects databases’, Appl. Intell., 2010, 33, (3), pp. 370–386 (doi: 10.1007/s10489-009-0173-z).
12. 12)
  - [4]. Chen, H., Li, W.: ‘Mobile device user's privacy security assurance behavior: A technology threat avoidance perspective’, Inf. Comput. Secur., 2017, 25, pp. 330–344 (doi: 10.1108/ICS-04-2016-0027).
13. 13)
  - [23]. Cen, Y., Zhang, J., Wang, G., et al: ‘Trust relationship prediction in alibaba e-commerce platform’, IEEE Trans. Knowl. Data Eng., 2020, 32, (5), pp. 1024–1035 (doi: 10.1109/TKDE.2019.2893939).
14. 14)
  - [13]. Bishop, C.M.: ‘Pattern recognition and machine learning’ (Springer, Berlin, Germany, 2006).
15. 15)
  - [14]. Li, N., Chen, G.: ‘Multi-layered friendship modeling for location-based mobile social networks’. In: Proc. of the 6th Annual Int. Conf. on Mobile and Ubiquitous Systems: Computing, Networking and Services, MOBIQUITOUS, Toronto, Canada, July 13–16 2009, pp. 1–10.
16. 16)
  - [2]. Fernandes, E., Rahmati, A., Jung, J., et al: ‘Security implications of permission models in smart-home application frameworks’, IEEE Secur. Privacy, 2017, 15, (2), pp. 24–30 (doi: 10.1109/MSP.2017.43).
17. 17)
  - [12]. Qiao, S., Han, N., Zhu, W., et al: ‘Traplan: an effective three-in-one trajectory-prediction model in transportation networks’, IEEE Trans. Intell. Transp. Syst., 2015, 16, (3), pp. 1188–1198 (doi: 10.1109/TITS.2014.2353302).
18. 18)
  - [11]. Qiao, S., Han, N., Gao, Y., et al: ‘A fast parallel community discovery model on complex networks through approximate optimization’, IEEE Trans. Knowl. Data Eng., 2018, 30, (9), pp. 1638–1651 (doi: 10.1109/TKDE.2018.2803818).
19. 19)
  - [5]. Papageorgiou, A., Strigkos, M., Politou, E., et al: ‘Security and privacy analysis of mobile health applications: the alarming state of practice’, IEEE Access, 2018, 6, pp. 9390–9403 (doi: 10.1109/ACCESS.2018.2799522).
20. 20)
  - [30]. Liben Nowell, D., Novak, J., Kumar, R., et al: ‘Geographic routing in social networks’, Proc. Natl. Acad. Sci., 2005, 102, (33), pp. 11623–11628 (doi: 10.1073/pnas.0503018102).
21. 21)
  - [1]. Obiri-Yeboah, J., Man, Q.: ‘Data security of android applications’. In: Proc. of Int. Conf. on Natural Computation & Fuzzy Systems & Knowledge Discovery. (IEEE), Changsha, China, 2016, pp. 8–14.
22. 22)
  - [27]. Chen, Z., Shen, H.T., Zhou, X., et al: ‘Searching trajectories by locations: an efficiency study’. Proc. of the 2010 ACM SIGMOD Int. Conf. on Management of Data. (ACM), Indianapolis, USA, 2010, pp. 255–266.
23. 23)
  - [22]. Tang, W., Zhuang, H., Tang, J.: ‘Learning to infer social ties in large networks’. In: Joint European Conf. on Machine Learning and Knowledge Discovery in Databases, Athens, Greece, 2011, pp. 381–397.
24. 24)
  - [28]. Agarwal, P.K., Avraham, R.B., Kaplan, H., et al: ‘Computing the discrete fréchet distance in subquadratic time’, SIAM J. Comput., 2014, 43, (2), pp. 429–449 (doi: 10.1137/130920526).
25. 25)
  - [18]. Valverde Rebaza, J., Roche, M., Poncelet, P., et al: ‘Exploiting social and mobility patterns for friendship prediction in location-based social networks’. Proc. of the 2016 23rd Int. Conf. on Pattern Recognition (ICPR). (IEEE), Cancun, Mexico, 2016, pp. 2526–2531.
26. 26)
  - [33]. Bayrak, A.E., Polat, F.: ‘Effective feature reduction for link prediction in location-based social networks’, J. Inf. Sci., 2019, 45, (5), pp. 676–690 (doi: 10.1177/0165551518808200).
27. 27)
  - [24]. Cho, E., Myers, S.A., Leskovec, J.: ‘Friendship and mobility: user movement in location-based social networks’. Proc. of the 17th ACM SIGKDD int. Conf. on Knowledge Discovery and Data Mining. (ACM), San Diego California, USA, 2011, pp. 1082–1090.
28. 28)
  - [12]. Xu Rui, G., Li, W., Wei Li, W.: ‘Using multi-features to recommend friends on location-based social networks’, Peer-to-Peer Netw. Appl., 2017, 10, (6), pp. 1323–1330 (doi: 10.1007/s12083-016-0489-5).
29. 29)
  - [17]. Roth, M., Ben David, A., Deutscher, D., et al: ‘Suggesting friends using the implicit social graph’. Proc. of the Washington int. Conf. on Knowledge discovery and data mining. (ACM), Washington, USA, 2010, pp. 233–242.
30. 30)
  - [32]. Zhu, X., Ghahramani, Z., Lafferty, J.D.: ‘Semi-supervised learning using Gaussian fields and harmonic functions’. In: Proc. of the 20th Int. Conf. on Machine Learning (ICML-03). (Washington DC), Washington, DC, USA, 2003, pp. 912–919.
31. 31)
  - [8]. Qiao, S., Han, N., Zhou, J., et al: ‘Socialmix: a familiarity-based and preference-aware location suggestion approach’, Eng. Appl. Artif. Intell., 2018, 68, pp. 192–204 (doi: 10.1016/j.engappai.2017.11.006).
32. 32)
  - [15]. Bagci, H., Karagoz, P.: ‘Context-aware friend recommendation for location based social networks using random walk’. Proc. of the 25th Int. Conf. Companion on World Wide Web. (Republic and Canton of Geneva, CHE: Int. World Wide Web Conf.s Steering Committee), Montreal Quebec, Canada, 2016, pp. 531–536.
33. 33)
  - [29]. Mazumdar, P., Patra, B.K., Babu, K.S., et al: ‘Hidden location prediction using check-in patterns in location-based social networks’, Knowl. Inf. Syst., 2018, 57, (3), pp. 571–601 (doi: 10.1007/s10115-018-1170-5).

Friendship prediction model based on factor graphs integrating geographical location

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