Spotting misbehaviors in location-based social networks using tensors

Evangelos Papalexakis, Konstantinos Pelechrinis, Christos Faloutsos
2014 Proceedings of the 23rd International Conference on World Wide Web - WWW '14 Companion  
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
more » ... ers 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.
doi:10.1145/2567948.2576950 dblp:conf/www/PapalexakisPF14 fatcat:ecjxz63bmnasbalnsodxpad74m