Epidemic Diffusion of Social Updates in Dunbar-Based DOSN

Distributed Online Social Networks (DOSNs) do not rely on a central repository for storing social data so that the users can keep control of their private data and do not depend on the social network provider. The ego network, i.e. the network made up of an individual, the ego, along with all the social ties she has with other people, the alters, may be exploited to define distributed social overlays and dissemination protocols. In this paper we propose a new epidemic protocol able to spread social updates in Dunbar-based 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 egocentric social measure approximating the Betweenness Centrality. The computation of the WEBC exploits a weighted graph where the weights correspond to the tie strengths between the users so that nodes having a higher number of interactions are characterized by a higher value of the WEBC. A set of experimental results proving the effectiveness of our approach is presented

A P2P architecture for Distributed Dunbar-based Social Networks

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