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政大典藏 > College of Informatics > Department of Computer Science > Periodical Articles > Item 140.119/111610

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/111610

Title:	Exploring check-in data to infer social ties in location based social networks
Authors:	Njoo, Gunarto Sindoro;Kao, Min-Chia;Hsu, Kuo-Wei;Peng, Wen-Chih 徐國偉
Contributors:	資訊科學系
Keywords:	Location;Network function virtualization;Social networking (online);Derived features;Location data;Location-based social networks;Mobility pattern;Social connection;Social networking services;Spatial-temporal features;State-of-the-art methods;Data mining
Date:	2017
Issue Date:	2017-08-02 16:07:28 (UTC+8)
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. © 2017, Springer International Publishing AG.
Relation:	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10234 LNAI, 460-471
Data Type:	book/chapter
DOI link:	http://dx.doi.org/10.1007/978-3-319-57454-7_36
DOI:	10.1007/978-3-319-57454-7_36
Appears in Collections:	[Department of Computer Science ] Periodical Articles

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