Selective Propagation of Social Data in Decentralized Online Social Network

  • Udeep Tandukar
  • Julita Vassileva
Conference paper

DOI: 10.1007/978-3-642-28509-7_20

Volume 7138 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
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

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.

Keywords

Online Social Network Decentralization Information propagation Relationship modeling 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Udeep Tandukar
    • 1
  • Julita Vassileva
    • 1
  1. 1.Department of Computer ScienceUniversity of SaskatchewanSaskatoonCanada