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Classifying trust/distrust relationships in online social networks
Abstract Online social networks are increasingly beOnline social networks are increasingly being used as places where communities gather to exchange information, form opinions, collaborate in response to events. An aspect of this information exchange is how to determine if a source of social information can be trusted or not. Data mining literature addresses this problem. However, if usually employs social balance theories, by looking at small structures in complex networks known as triangles. This has proven effective in some cases, but it under performs in the lack of context information about the relation and in more complex interactive structures. In this paper we address the problem of creating a framework for the trust inference, able to infer the trust/distrust relationships in those relational environments that cannot be described by using the classical social balance theory. We do so by decomposing a trust network in its ego network components and mining on this ego network set the trust relationships, extending a well known graph mining algorithm. We test our framework on three public datasets describing trust relationships in the real world (from the social media Epinions, Slash dot and Wikipedia) and confronting our results with the trust inference state of the art, showing better performances where the social balance theory fails.ces where the social balance theory fails.
Abstractsub Online social networks are increasingly beOnline social networks are increasingly being used as places where communities gather to exchange information, form opinions, collaborate in response to events. An aspect of this information exchange is how to determine if a source of social information can be trusted or not. Data mining literature addresses this problem. However, if usually employs social balance theories, by looking at small structures in complex networks known as triangles. This has proven effective in some cases, but it under performs in the lack of context information about the relation and in more complex interactive structures. In this paper we address the problem of creating a framework for the trust inference, able to infer the trust/distrust relationships in those relational environments that cannot be described by using the classical social balance theory. We do so by decomposing a trust network in its ego network components and mining on this ego network set the trust relationships, extending a well known graph mining algorithm. We test our framework on three public datasets describing trust relationships in the real world (from the social media Epinions, Slash dot and Wikipedia) and confronting our results with the trust inference state of the art, showing better performances where the social balance theory fails.ces where the social balance theory fails.
Bibtextype inproceedings  +
Doi 10.1109/SocialCom-PASSAT.2012.115  +
Has author Bachi G. + , Coscia M. + , Monreale A. + , Giannotti F. +
Has extra keyword Better performance + , Context information + , Ego networks + , Graph mining + , Information exchanges + , On-line social networks + , Social balances + , Social information + , Social media + , Social Networks + , State of the art + , Trust + , Trust inferences + , Trust networks + , Trust relationship + , Wikipedia + , Algorithms + , Data mining + , Information dissemination + , Mining + , Social networking (online) +
Has keyword Graph mining + , Mining + , Social network + , Trust +
Isbn 9780769548487  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 552–557  +
Published in Proceedings - 2012 ASE/IEEE International Conference on Privacy, Security, Risk and Trust and 2012 ASE/IEEE International Conference on Social Computing, SocialCom/PASSAT 2012 +
Title Classifying trust/distrust relationships in online social networks +
Type conference paper  +
Year 2012 +
Creation dateThis property is a special property in this wiki. 7 November 2014 09:47:09  +
Categories Publications without license parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Conference papers  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 7 November 2014 09:47:09  +
DateThis property is a special property in this wiki. 2012  +
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