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題名 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
貢獻者 資訊科學系
關鍵詞 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
上傳時間 2-Aug-2017 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
資料類型 book/chapter
DOI http://dx.doi.org/10.1007/978-3-319-57454-7_36
dc.contributor 資訊科學系zh_Tw
dc.creator (作者) Njoo, Gunarto Sindoro;Kao, Min-Chia;Hsu, Kuo-Wei;Peng, Wen-Chihen_US
dc.creator (作者) 徐國偉zh_TW
dc.date (日期) 2017en_US
dc.date.accessioned 2-Aug-2017 16:07:28 (UTC+8)-
dc.date.available 2-Aug-2017 16:07:28 (UTC+8)-
dc.date.issued (上傳時間) 2-Aug-2017 16:07:28 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/111610-
dc.description.abstract (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.en_US
dc.format.extent 110 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10234 LNAI, 460-471en_US
dc.subject (關鍵詞) 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 miningen_US
dc.title (題名) Exploring check-in data to infer social ties in location based social networksen_US
dc.type (資料類型) book/chapter
dc.identifier.doi (DOI) 10.1007/978-3-319-57454-7_36
dc.doi.uri (DOI) http://dx.doi.org/10.1007/978-3-319-57454-7_36