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    题名: Exploring check-in data to infer social ties in location based social networks
    作者: Njoo, Gunarto Sindoro;Kao, Min-Chia;Hsu, Kuo-Wei;Peng, Wen-Chih
    徐國偉
    贡献者: 資訊科學系
    关键词: 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
    日期: 2017
    上传时间: 2017-08-02 16:07:28 (UTC+8)
    摘要: 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.
    關聯: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10234 LNAI, 460-471
    数据类型: book/chapter
    DOI 連結: http://dx.doi.org/10.1007/978-3-319-57454-7_36
    DOI: 10.1007/978-3-319-57454-7_36
    显示于类别:[資訊科學系] 期刊論文

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