@inproceedings{a215b082c5d9406a8c661eec5f759369,
title = "Exploring check-in data to infer social ties in location based social networks",
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{\textquoteright} check-in data. We derive three key features from users{\textquoteright} meeting events and propose a framework called SCI framework (Social Connection Inference framework) which integrates all derived features to differentiate coincidences from real friends{\textquoteright} meetings. Extensive experiments on two location-based social network datasets show that the proposed SCI framework can outperform the state-of-the-art method.",
author = "Njoo, {Gunarto Sindoro} and Kao, {Min Chia} and Hsu, {Kuo Wei} and Wen-Chih Peng",
year = "2017",
month = jan,
day = "1",
doi = "10.1007/978-3-319-57454-7_36",
language = "English",
isbn = "9783319574530",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "460--471",
editor = "Kyuseok Shim and Jae-Gil Lee and Longbing Cao and Xuemin Lin and Jinho Kim and Yang-Sae Moon",
booktitle = "Advances in Knowledge Discovery and Data Mining - 21st Pacific-Asia Conference, PAKDD 2017, Proceedings",
address = "Germany",
note = "null ; Conference date: 23-05-2017 Through 26-05-2017",
}