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2017 | OriginalPaper | Buchkapitel

Exploring Check-in Data to Infer Social Ties in Location Based Social Networks

verfasst von : Gunarto Sindoro Njoo, Min-Chia Kao, Kuo-Wei Hsu, Wen-Chih Peng

Erschienen in: Advances in Knowledge Discovery and Data Mining

Verlag: Springer International Publishing

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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.

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Vorheriges Kapitel Sparse Stochastic Inference with Regularization

Nächstes Kapitel Scalable Twitter User Clustering Approach Boosted by Personalized PageRank

We have also evaluated the performances in the various distance thresholds \(\varDelta \) to \(\lbrace 0\,m, 250\,m, 500\,m, 750\,m, 1000\,m \rbrace \) in the preliminary experiments. The number of retrieved friendships in higher \(\varDelta \) is slightly higher (up to 1.5 times to \(\varDelta \ =\ \)0 m). However, the overall prediction performance is similar to \(\varDelta \ =\ \)0 m.

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Titel: Exploring Check-in Data to Infer Social Ties in Location Based Social Networks
verfasst von: Gunarto Sindoro Njoo
Min-Chia Kao
Kuo-Wei Hsu
Wen-Chih Peng
Verlag: Springer International Publishing
Buch: Advances in Knowledge Discovery and Data Mining
Print ISBN: 978-3-319-57453-0

Electronic ISBN: 978-3-319-57454-7

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-57454-7_36

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