Abstract
Social Networking Services (SNS), such as Facebook, Twitter, and Foursquare, allow users to perform check-in and share their location data. Given the check-in data records, we can extract the features (e.g., the spatial-temporal features) to infer the social ties. The challenge of this inference task is to differentiate between real friends and strangers by solely observing their mobility patterns. In this paper, we explore the meeting events or co-occurrences from users’ check-in data. We derive three key features from users’ meeting events and propose a framework called SCI framework (Social Connection Inference framework) which integrates all derived features to differentiate coincidences from real friends’ meetings. Extensive experiments on two location-based social network datasets show that the proposed SCI framework can outperform the state-of-the-art method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
We have also evaluated the performances in the various distance thresholds \(\varDelta \) to \(\lbrace 0\,m, 250\,m, 500\,m, 750\,m, 1000\,m \rbrace \) in the preliminary experiments. The number of retrieved friendships in higher \(\varDelta \) is slightly higher (up to 1.5 times to \(\varDelta \ =\ \)0 m). However, the overall prediction performance is similar to \(\varDelta \ =\ \)0 m.
References
Cheng, R., Pang, J., Zhang, Y.: Inferring friendship from check-in data of location-based social networks. In: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, ASONAM 2015, pp. 1284–1291. ACM, New York (2015)
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, UbiComp 2010, pp. 119–128. ACM, New York (2010)
Hsieh, H.-P., Yan, R., Li, C.-T.: Where you go reveals who you know: analyzing social ties from millions of footprints. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, CIKM 2015, pp. 1839–1842. ACM, New York (2015)
Leskovec, J., Krevl, A.: SNAP datasets: Stanford large network dataset collection. http://snap.stanford.edu/data.
Lin, J.: Divergence measures based on the Shannon entropy. IEEE Trans. Inf. Theor. 37(1), 145–151 (2006)
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Pham, H., Shahabi, C., Liu, Y.: EBM: An entropy-based model to infer social strength from spatiotemporal data. In: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, SIGMOD 2013, pp. 265–276. ACM, New York (2013)
Sadilek, A., Kautz, H., Bigham, J.P.: Finding your friends and following them to where you are. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, WSDM 2012, pp. 723–732. ACM, New York (2012)
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, pp. 1046–1054. ACM (2011)
Sintos, S., Tsaparas, P.: Using strong triadic closure to characterize ties in social networks. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2014, pp. 1466–1475. ACM, New York (2014)
Wang, H., Li, Z., Lee, W.-C.: PGT: measuring mobility relationship using personal, global and temporal factors. In: 2014 IEEE International Conference on Data Mining, pp. 570–579. IEEE (2014)
Wiese, J., Min, J.-K., Hong, J.I., Zimmerman, J.: You never call, you never write: call and SMS logs do not always indicate tie strength. In: Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing, pp. 765–774. ACM (2015)
Zhu, W.-Y., Peng, W.-C., Chen, L.-J., Zheng, K., Zhou, X.: Modeling user mobility for location promotion in location-based social networks. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2015, pp. 1573–1582. ACM, New York (2015)
Acknowledgements
Wen-Chih Peng was partially support by the TAIWAN MOST (104-2221-E-009-138-MY2 and 105-2634-E-009-002) and Academic Sinica Theme project No. AS-105-TP-A07.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Njoo, G.S., Kao, MC., Hsu, KW., Peng, WC. (2017). Exploring Check-in Data to Infer Social Ties in Location Based Social Networks. In: Kim, J., Shim, K., Cao, L., Lee, JG., Lin, X., Moon, YS. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2017. Lecture Notes in Computer Science(), vol 10234. Springer, Cham. https://doi.org/10.1007/978-3-319-57454-7_36
Download citation
DOI: https://doi.org/10.1007/978-3-319-57454-7_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-57453-0
Online ISBN: 978-3-319-57454-7
eBook Packages: Computer ScienceComputer Science (R0)