WebDAV: a network protocol for remote collaborative authoring on the Web
Proceedings of the Sixth European conference on Computer supported cooperative work
A scalable content-addressable network
Proceedings of the 2001 conference on Applications, technologies, architectures, and protocols for computer communications
OpenDHT: a public DHT service and its uses
Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
Proceedings of the 2007 ACM conference on Recommender systems
TRIBLER: a social-based peer-to-peer system: Research Articles
Concurrency and Computation: Practice & Experience - Recent Advances in Peer-to-Peer Systems and Security (P2P 2006)
Push-Poll Recommender System: Supporting Word of Mouth
UM '07 Proceedings of the 11th international conference on User Modeling
PeerSoN: P2P social networking: early experiences and insights
Proceedings of the Second ACM EuroSys Workshop on Social Network Systems
UMAP'11 Proceedings of the 19th international conference on Advances in User Modeling
Ensuring relevant and serendipitous information flow in decentralized online social network
AIMSA'12 Proceedings of the 15th international conference on Artificial Intelligence: methodology, systems, and applications
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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.