Skip to main content
SpringerLink
Log in

Co-occurrence prediction in a large location-based social network

  • Research Article
  • Published:
Frontiers of Computer Science Aims and scope Submit manuscript

Abstract

Location-based social network (LBSN) is at the forefront of emerging trends in social network services (SNS) since the users in LBSN are allowed to “check-in” the places (locations) when they visit them. The accurate geographical and temporal information of these check-in actions are provided by the end-user GPS-enabled mobile devices, and recorded by the LBSN system. In this paper, we analyze and mine a big LBSN data, Gowalla, collected by us. First, we investigate the relationship between the spatio-temporal co-occurrences and social ties, and the results show that the co-occurrences are strongly correlative with the social ties. Second, we present a study of predicting two users whether or not they will meet (co-occur) at a place in a given future time, by exploring their check-in habits. In particular, we first introduce two new concepts, bag-of-location and bag-of-time-lag, to characterize user’s check-in habits. Based on such bag representations, we define a similarity metric called habits similarity to measure the similarity between two users’ check-in habits. Then we propose a machine learning formula for predicting co-occurrence based on the social ties and habits similarities. Finally, we conduct extensive experiments on our dataset, and the results demonstrate the effectiveness of the proposed method.

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

Similar content being viewed by others

References

  1. Hey T, Tansley S, Tolle K M. The fourth paradigm: data-intensive scientific discovery. Microsoft Research, 2009

    Google Scholar 

  2. Crandall D J, Backstrom L, Cosley D, Suri S, Huttenlocher D, Kleinberg J. Inferring social ties from geographic coincidences. Proceedings of the National Academy of Sciences, 2010, 107(52): 22436–22441

    Article  Google Scholar 

  3. Lauw H W, Lim E P, Pang H, Tan T T. Social network discovery by mining spatio-temporal events. Computational & Mathematical Organization Theory, 2005, 11(2): 97–118

    Article  MATH  Google Scholar 

  4. Lauw H W, Lim E P, Pang H, Tan T T. Stevent: spatio-temporal event model for social network discovery. ACM Transactions on Information Systems (TOIS), 2010, 28(3): 15:1–15:32

    Article  Google Scholar 

  5. Milgram S. The experience of living in cities. Science, 1970, 167(3924): 1461–1468

    Article  Google Scholar 

  6. Li Q, Zheng Y, Xie X, Chen Y, Liu W, Ma WY. Mining user similarity based on location history. In: Proceedings of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. 2008, 34–44

    Google Scholar 

  7. Christopher D, Manning P R, Schütze H. Introduction to Information Retrieval. Cambridge University Press, 2008

    MATH  Google Scholar 

  8. Ye M, Yin P, Lee W C. Location recommendation for location-based social networks. In: Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems. 2010, 458–461

    Google Scholar 

  9. Li N, Chen G. Analysis of a location-based social network. In: Proccedings of the 2009 International Conference on Computational Science and Engineering. 2009, 263–270

    Chapter  Google Scholar 

  10. Li N, Chen G. Sharing location in online social networks. IEEE Network, 2010, 24(5): 20–25

    Article  Google Scholar 

  11. Scellato S, Mascolo C, Musolesi M, Latora V. Distance matters: geosocial metrics for online social networks. In: Proceedings of the 3rd Conference on Online Social Networks. 2010, 8–17

    Google Scholar 

  12. Scellato S, Noulas A, Lambiotte R, Mascolo C. Socio-spatial properties of online location-based social networks. In: Proceedings of the 5th International AAAI Conference on Weblogs and Social Media. 2011, 1–8

    Google Scholar 

  13. 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. 2011, 1046–1054

    Google Scholar 

  14. Cho E, Myers S A, Leskovec J. Friendship and mobility: user movement in location-based social networks. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2011, 1082–1090

    Google Scholar 

  15. Liben-Nowell D, Kleinberg J. The link-prediction problem for social networks. Journal of the American Society for Information Science and Technology, 2007, 58(7): 1019–1031

    Article  Google Scholar 

  16. Ito T, Shimbo M, Kudo T, Matsumoto Y. Application of kernels to link analysis. In: Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining. 2005, 586–592

    Google Scholar 

  17. Kunegis J, Lommatzsch A. Learning spectral graph transformations for link prediction. In: Proceedings of the 26th Annual International Conference on Machine Learning. 2009, 561–568

    Google Scholar 

  18. Li R H, Yu J X, Liu J. Link prediction: the power of maximal entropy random walk. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. 2011, 1147–1156

    Google Scholar 

  19. Leroy V, Cambazoglu B B, Bonchi F. Cold start link prediction. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2010, 393–402

    Chapter  Google Scholar 

  20. Backstrom L, Leskovec J. Supervised random walks: predicting and recommending links in social networks. In: Proceedings of the 4th ACM International Conference on Web Search and DataMining. 2011, 635–644

    Chapter  Google Scholar 

  21. Lichtenwalter R N, Lussier J T, Chawla N V. New perspectives and methods in link prediction. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2010, 243–252

    Chapter  Google Scholar 

  22. Cranshaw J, Toch E, Hong J, Kittur A, Sadeh N. Bridging the gap between physical location and online social networks. In: Proceedings of the 12th ACM International Conference on Ubiquitous Computing. 2010, 119–128

    Chapter  Google Scholar 

  23. Mamoulis N, Cao H, Kollios G, Hadjieleftheriou M, Tao Y, Cheung D W. Mining, indexing, and querying historical spatiotemporal data. In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2004, 236–245

    Google Scholar 

  24. Cao H, Mamoulis N, Cheung D W. Mining frequent spatio-temporal sequential patterns. In: Proccedings of the 5th IEEE International Conference on Data Mining. 2005, 8–16

    Google Scholar 

  25. Verhein F. Mining complex spatio-temporal sequence patterns. In: Proceedings of the 2009 SIAM International Conference on Data Mining. 2009, 605–617

    Google Scholar 

  26. Celik M, Shekhar S, Rogers J P, Shine J A. Mixed-drove spatiotemporal co-occurrence pattern mining. IEEE Transactions on Knowledge and Data Engineering, 2008, 20(10): 1322–1335

    Article  Google Scholar 

  27. Li Z, Ding B, Han J, Kays R, Nye P. Mining periodic behaviors for moving objects. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2010, 1099–1108

    Chapter  Google Scholar 

  28. Mei Q, Liu C, Su H, Zhai C. A probabilistic approach to spatiotemporal theme pattern mining on weblogs. In: Proceedings of the 15th International Conference on World Wide Web. 2006, 533–542

    Chapter  Google Scholar 

  29. Farrahi K, Gatica-Perez D. Probabilistic mining of socio-geographic routines from mobile phone data. IEEE Journal of Selected Topics in Signal Processing, 2010, 4(4): 746–755

    Article  Google Scholar 

  30. Farrahi K, Gatica-Perez D. Discovering routines from large-scale human locations using probabilistic topic models. ACM Transactions on Intelligent Systems and Technology, 2011, 2(1): 3:1–3:27

    Article  Google Scholar 

  31. Li P, König A C. Theory and applications of b-bit minwise hashing. Communications of the ACM, 2011, 54(8): 101–109

    Article  Google Scholar 

  32. Li P, Shrivastava A, Moore J L, König A C. Hashing algorithms for large-scale learning. In: Proceedings of the 25th Annual Conference on Neural Information Processing Systems. 2011

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of System Engineering and Engineering Management, The Chinese University of Hong Kong, Hong Kong, China

    Rong-Hua Li & Jeffrey Xu Yu

  2. Faculty of Engineering, Information and systems, University of Tsukuba, Ibaraki, 305-8577, Japan

    Jianquan Liu, Hanxiong Chen & Hiroyuki Kitagawa

  3. Cloud System Research Laboratories, NEC Corporation, Tokyo, 108-8001, Japan

    Jianquan Liu

Authors
  1. Rong-Hua Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jianquan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jeffrey Xu Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hanxiong Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hiroyuki Kitagawa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rong-Hua Li.

Additional information

Rong-Hua LI is pursuing his PhD in Department of System Engineering and Engineering Management, The Chinese University of Hong Kong, China. His research interests include social network analysis and mining, complex network theory, uncertain graphs mining, Monte-Carlo algorithms, and machine learning.

Jianquan LIU received the BE from Shantou University, China, ME and PhD from the University of Tsukuba, Japan, in 2005, 2009, and 2012, respectively. He was a development engineer in Tencent Inc. from 2005 to 2006. He is currently a researcher at the Cloud System Research Laboratories of NEC Corporation, working on the topics of large-scale data processing and cloud computing. His research interests include high-dimensional similarity search, social network analysis, web data mining and information retrieval, cloud storage and computing, and multimedia databases. He is a member of ACM and the Database Society of Japan (DBSJ).

Jeffrey Xu YU received the BE, ME, and PhD in Computer Science from the University of Tsukuba, Japan, in 1985, 1987, and 1990, respectively. He held teaching positions in the Institute of Information Sciences and Electronics, University of Tsukuba, Japan, and the Department of Computer Science, The Australian National University. Currently, he is a professor in the Department of Systems Engineering and Engineering Management, the Chinese University of Hong Kong. His current main research interest includes graph database, graph mining, keyword search in relational databases, and social network analysis. He is a senior member of IEEE, a member of IEEE Computer Society, and a member of ACM.

Hanxiong CHEN received the BS from Zhongshan University, Guangdong, China, in 1985, the MS and the PhD in Computer Science, from the University of Tsukuba, Japan, in 1990 and 1993, respectively. He is currently an assistant professor at Faculty of Engineering, Information and Systems, University of Tsukuba. His research interests include data engineering, knowledge discovery, data mining, and information retrieval. He is a member of ACM, IEEE-CS, and IPSJ.

Hiroyuki KITAGAWA received the BS in Physics and the MS and PhD in Computer Science, all from the University of Tokyo, in 1978, 1980, and 1987, respectively. He is currently a full professor at Faculty of Engineering, Information and Systems, University of Tsukuba. His research interests include data integration, data mining, information retrieval, stream processing, data-intensive computing, XML, and scientific databases. He is an editorial board member of IEEE TKDE and WWWJ, a Fellow of IPSJ and IEICE, Vice Chairperson of the Database Society of Japan, and a member of ACM, IEEE Computer Society, and JSSST.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Li, RH., Liu, J., Yu, J.X. et al. Co-occurrence prediction in a large location-based social network. Front. Comput. Sci. 7, 185–194 (2013). https://doi.org/10.1007/s11704-013-3902-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11704-013-3902-8

Keywords

Search

Navigation