Abstract
In Online Social Networks (OSNs) users are overwhelmed with huge amount of social data, most of which are irrelevant to their interest. Due to the fact that most current OSNs are centralized, people are forced to share their data with the site, in order to be able to share it with their friends, and thus they lose control over it. Decentralized OSNs provide an alternative by allowing users to maintain control over their data. This paper proposes a decentralized OSN architecture to deal with this problem and an approach for propagation of social data in a decentralized OSN that reduces irrelevant data among users. The approach uses interaction between users to construct relationship model of interest, which acts as a filter later while propagating social data of the same interest group. This paper also presents the design of a simulation to analyze the scalability and rate of system learning (convergence) of the system using the relationship model.
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Tandukar, U., Vassileva, J. (2012). Selective Propagation of Social Data in Decentralized Online Social Network. In: Ardissono, L., Kuflik, T. (eds) Advances in User Modeling. UMAP 2011. Lecture Notes in Computer Science, vol 7138. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28509-7_20
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DOI: https://doi.org/10.1007/978-3-642-28509-7_20
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