Activity location inference of users based on social relationship

Nur Al Hasan Haldar, Mark Reynolds, Quanxi Shao, Cecile Paris, Jianxin Li, Yunliang Chen

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

Users in social networks often form relationships with other users who participate together in various activities nearby. The activity locations which are frequently shared with the friends are important in real life in order to understand the precise spatial space of the social users. However, the locations of individuals in a social network are often unknown. This is because the social users do not bother to broadcast their locations in public due to many reasons including privacy. Identifying the top activity location of a user at a higher granularity level will improve various community based applications like Meetup, Groupon, etc. In this paper, we propose a method to infer the top activity location of social users using the implicit information available in the network. Our proposed approach can estimate the activity location of a user by propagating the spatial information of the neighbors through friendship edges. We maintain a proper inference sequence to propagate the location labels of the users. We find that the proposed method has significantly improved the state-of-the-art network based location inference techniques in terms of both the accuracy and efficiency. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Original languageEnglish
Pages (from-to)1165 - 1183
Number of pages19
JournalWorld Wide Web
Volume24
Issue number4
DOIs
Publication statusPublished - Jul 2021

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