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Digital archive of theses discussed at the University of Pisa


Thesis etd-11202015-225158

Thesis type
Tesi di dottorato di ricerca
Thesis title
A P2P architecture for Distributed Dunbar-based Social Networks
Academic discipline
Course of study
tutor Prof.ssa Ricci, Laura Emilia Maria
relatore Dott. Conti, Marco
  • data availability
  • Distributed Online Social Networks
  • Dunbar
  • Online Social Networks
  • P2P
Graduation session start date
Online Social Networks (OSNs) are becoming more and more popular on the Web. Distributed Online Social Networks (DOSNs) are OSNs which do not exploit a central server for storing users’ data and enable users to have more control on their profile content, ensuring a higher level of privacy. In this thesis we propose DiDuSoNet, a novel P2P Distributed Dunbar-based Online Social Network where users can exercise full access control on their data. Our system exploits trust relationships over the novel Dunbar-based Social Overlay for providing a set of important social services like information diffusion and data availability. In particular, our system manages the problem of data availability by proposing two P2P dynamic trusted storage approaches. By following the Dunbar concept, our system stores the data of a user only on friend nodes, which have regular contacts with it. Differently from other approaches, nodes chosen to keep data replicas are not statically defined but dynamically change according to users’ churn. Furthermore, the system provides a new epidemic protocol able to spread social updates in DOSN overlays, where the links between nodes are defined by considering the social interactions between users. Our approach is based on the notion of Weighted Ego Betweenness Centrality (WEBC), which is an ego-centric social measure approximating the Betweenness Centrality. The weights considered in the computation of the WEBC correspond to the tie strength between friends so that nodes having a higher number of interactions are characterized by an higher value of the WEBC. The lack of real dataset containing structural and temporal information of users is the main limitation to the research on this field. To fill the gap, we have developed a Facebook application and we have crawled data about more than 300 Facebook users. We have studied the dataset in deep to obtain useful information to characterize OSNs users not only under the structural point of view, but also to understand the user behaviors in term of session length. A set of experimental results, conducted by using our Facebook dataset and an old Facebook Regional Network dataset, proving the effectiveness of our system are presented.