Exploring check-in data to infer social ties in location based social networks

Gunarto Sindoro Njoo*, Min Chia Kao, Kuo Wei Hsu, Wen-Chih Peng

*此作品的通信作者

研究成果: Conference contribution同行評審

11 引文 斯高帕斯(Scopus)

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.

原文English
主出版物標題Advances in Knowledge Discovery and Data Mining - 21st Pacific-Asia Conference, PAKDD 2017, Proceedings
編輯Kyuseok Shim, Jae-Gil Lee, Longbing Cao, Xuemin Lin, Jinho Kim, Yang-Sae Moon
發行者Springer Verlag
頁面460-471
頁數12
ISBN(列印)9783319574530
DOIs
出版狀態Published - 2017
事件21st Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2017 - Jeju, Korea, Republic of
持續時間: 23 5月 201726 5月 2017

出版系列

名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
10234 LNAI
ISSN(列印)0302-9743
ISSN(電子)1611-3349

Conference

Conference21st Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2017
國家/地區Korea, Republic of
城市Jeju
期間23/05/1726/05/17

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