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.
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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.
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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.
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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
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