Publication
Title
Effective and efficient location influence mining in location-based social networks
Author
Abstract
Location-based social networks (LBSN) are social networks complemented with location data such as geo-tagged activity data of its users. In this paper, we study how users of an LBSN are navigating between locations and based on this information we select the most influential locations. In contrast to existing works on influence maximization, we are not per se interested in selecting the users with the largest set of friends or the set of locations visited by the most users; instead, we introduce a notion of location influence that captures the ability of a set of locations to reach out geographically by utilizing their visitors as message carriers. We further capture the influence of these visitors on their friends in LBSNs and utilize them to predict the potential future location influence more accurately. We provide exact online algorithms and more memory efficient but approximate variants based on the HyperLogLog and the modified HyperLogLog sketch to maintain a data structure called Influence Oracle that allows to efficiently find a top-k set of influential locations. Experiments show that our new location influence notion favors diverse sets of locations with a large geographical spread and that our algorithms are efficient, scalable and allow to capture future location influence.
Language
English
Source (journal)
Knowledge and information systems: an international journal. - London
Knowledge and information systems: an international journal. - London
Publication
London : 2019
ISSN
0219-1377 [print]
0219-3116 [online]
Volume/pages
61 :1 (2019) , p. 327-362
ISI
000483698200013
Full text (Publisher's DOI)
Full text (publisher's version - intranet only)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 01.08.2018
Last edited 24.09.2021
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