Papalexakis, E and Pelechrinis, K and Faloutsos, C
(2014)
Spotting misbehaviors in location-based social networks using tensors.
In: UNSPECIFIED.
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Abstract
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)
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Status: |
Published |
Creators/Authors: |
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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 |
URI: |
http://d-scholarship.pitt.edu/id/eprint/21799 |
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