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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

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.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 21st Pacific-Asia Conference, PAKDD 2017, Proceedings
EditorsKyuseok Shim, Jae-Gil Lee, Longbing Cao, Xuemin Lin, Jinho Kim, Yang-Sae Moon
PublisherSpringer Verlag
Pages460-471
Number of pages12
ISBN (Print)9783319574530
DOIs
StatePublished - 1 Jan 2017
Event21st Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2017 - Jeju, Korea, Republic of
Duration: 23 May 201726 May 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10234 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference21st Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2017
CountryKorea, Republic of
CityJeju
Period23/05/1726/05/17

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