Hsun-Ping Hsieh
ABSTRACT
This paper aims to investigate how the geographical footprints of users correlate to their social ties. While conventional wisdom told us that the more frequently two users co-locate in geography, the higher probability they are friends, we find that in real geo-social data, Gowalla and Meetup, almost all of the user pairs with friendships had never met geographically. In this sense, can we discover social ties among users purely using their geographical footprints even if they never met? To study this question, we develop a two-stage feature engineering framework. The first stage is to characterize the direct linkages between users through their spatial co-locations while the second is to capture the indirect linkages between them via a co-location graph. Experiments conducted on Gowalla check-in data and Meetup meeting events exhibit not only the superiority of our feature model, but also validate the predictability (with 70% accuracy) of detecting social ties solely from user footprints.
- E. Cho, S. A. Myers, and J. Leskovec. Friendship and mobility: User movement in location-based social networks. In KDD, 2011. Google Scholar
Digital Library
- D. J. Crandall, L. Backstrom, D. Cosley, S. Suri, D. Huttenlocher, and J. Kleinberg. Inferring social ties from geographic coincidences. PNAS, 2010.Google ScholarCross Ref
- D. Liben-Nowell and J. Kleinberg. The link prediction problem for social networks. In CIKM, 2003. Google ScholarDigital Library
- H. Pham, C. Shahabi, and Y. Liu. Ebm: An entropy-based model to infer social strength from spatiotemporal data. In SIGMOD, 2013. Google ScholarDigital Library
- S. Scellato, A. Noulas, and C. Mascolo. Exploiting place features in link prediction on location-based social networks. In KDD, 2011. Google ScholarDigital Library
- H. Tong, C. Faloutsos, and J.-Y. Pan. Fast random walk with restart and its applications. In ICDM, 2006. Google ScholarDigital Library
- C. Wang, M. Ye, and W.-C. Lee. From face-to-face gathering to social structure. In CIKM, 2012. Google ScholarDigital Library
- D. Wang, D. Pedreschi, C. Song, F. Giannotti, and A.-L. Barabasi. Human mobility, social ties, and link prediction. In KDD, 2011. Google ScholarDigital Library
- H. Wang, Z. Li, and W.-C. Lee. Pgt: Measuring mobility relationship using personal, global and temporal factors. In ICDM, 2014. Google ScholarDigital Library
- X. Xiao, Y. Zheng, Q. Luo, and X. Xie. Inferring social ties between users with human location history. Journal of Ambient Intelligence and Humanized Computing, 5(1), 2014.Google ScholarCross Ref
- P. Yin, Q. He, X. Liu, and W.-C. Lee. It takes two to tango: Exploring social tie development with both online and offline interactions. In SDM, 2014.Google ScholarCross Ref
Index Terms
- Where You Go Reveals Who You Know: Analyzing Social Ties from Millions of Footprints
Recommendations
Inferring visiting time distributions of locations from incomplete check-in data
WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide WebOnline location-based services, such as Foursquare and Facebook, provide a great resource for location recommendation. As we know the time is one of the important factors on recommending places with proper time for users, since the pleasure of visiting ...
Location recommendation in location-based social networks using user check-in data
SIGSPATIAL'13: Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information SystemsThis paper studies the problem of recommending new venues to users who participate in location-based social networks (LBSNs). As an increasingly larger number of users partake in LBSNs, the recommendation problem in this setting has attracted ...
Learning from Your Friends' Check-Ins: An Empirical Study of Location-Based Social Networks
Recently, mobile applications have offered users the option to share their location information with friends. Using data from a major location-based social networking application in China, we estimate an empirical model of restaurant discovery and ...
Comments