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

Frontiers of Computer Science

Volumn 7, Issue 2, 2013, Pages 185-194

  Li, Rong Hua ^a   Liu, Jianquan ^b,c   Yu, Jeffrey Xu ^a   Chen, Hanxiong ^b   Kitagawa, Hiroyuki ^b  

^a CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

^b UNIVERSITY OF TSUKUBA   (Japan)

^c NEC CORPORATION   (Japan)

Author keywords

cooccurrence; Gowalla; location based social networks

Indexed keywords

CLUSTERING ALGORITHMS; FORECASTING; LOCATION; SOCIAL NETWORKING (ONLINE);

CO-OCCURRENCE; EMERGING TRENDS; GOWALLA; LOCATION-BASED SOCIAL NETWORKS; SIMILARITY METRICS; SOCIAL NETWORK SERVICE (SNS); SPATIO TEMPORAL; TEMPORAL INFORMATION;

LOCATION BASED SERVICES;

EID: 84875973433     PISSN: 20952228     EISSN: 20952236     Source Type: Journal    
DOI: 10.1007/s11704-013-3902-8     Document Type: Article

References (32)

1. Hey T, Tansley S, Tolle K M. The fourth paradigm: data-intensive scientific discovery. Microsoft Research, 2009.
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.
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.
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.
5. Milgram S. The experience of living in cities. Science, 1970, 167(3924): 1461-1468.
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.
7. Christopher D, Manning P R, Schütze H. Introduction to Information Retrieval. Cambridge University Press, 2008.
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.
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.
10. Li N, Chen G. Sharing location in online social networks. IEEE Network, 2010, 24(5): 20-25.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
25. Verhein F. Mining complex spatio-temporal sequence patterns. In: Proceedings of the 2009 SIAM International Conference on Data Mining. 2009, 605-617.
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.
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.
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.
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.
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.
31. Li P, König A C. Theory and applications of b-bit minwise hashing. Communications of the ACM, 2011, 54(8): 101-109.
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.