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Spotting misbehaviors in location-based social networks using tensors

Papalexakis, E and Pelechrinis, K and Faloutsos, C (2014) Spotting misbehaviors in location-based social networks using tensors. In: UNSPECIFIED.

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The proliferation of mobile devices that are capable of estimating their position, has lead to the emergence of a new class of social networks, namely location-based social networks (LBSNs for short). The main interaction between users in an LBSN is location sharing. While the latter can be realized through continuous tracking of a user's whereabouts from the service provider, the majority of LBSNs allow users to voluntarily share their location, through check-ins. LBSNs provide incentives to users to perform check-ins. However, these incentives can also lead to people faking their location, thus, generating false information. In this work, we propose the use of tensor decomposition for spotting anomalies in the check-in behavior of users. To the best of our knowledge, this is the first attempt to model this problem using tensor analysis.


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Item Type: Conference or Workshop Item (UNSPECIFIED)
Status: Published
CreatorsEmailPitt UsernameORCID
Papalexakis, E
Pelechrinis, Kkpele@pitt.eduKPELE0000-0002-6443-3935
Faloutsos, C
Date: 7 April 2014
Date Type: Publication
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Journal or Publication Title: WWW 2014 Companion - Proceedings of the 23rd International Conference on World Wide Web
Page Range: 551 - 552
Event Type: Conference
DOI or Unique Handle: 10.1145/2567948.2576950
Institution: University of Pittsburgh
Schools and Programs: School of Information Sciences > Telecommunications
Refereed: Yes
ISBN: 9781450327459
Date Deposited: 19 Jun 2014 15:08
Last Modified: 30 Mar 2021 10:55


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