Social applications mine user social graphs to improve performance in search, provide recommendations, allow resource sharing and increase data privacy. When such applications are implemented on a peer-to-peer (P2P) architecture, the social graph is distributed on the P2P system: the traversal of the social graph translates into a socially-informed routing in the peer-to-peer layer. In this work we introduce the model of a projection graph that is the result of decentralizing a social graph onto a peer-to-peer network. We focus on three social network metrics: degree, node betweenness and edge betweenness centrality and analytically formulate the relation between metrics in the social graph and in the projection graph. Through experimental evaluation on real networks, we demonstrate that when mapping user communities of sizes up to 50-150 users on each peer, the association between the properties of the social graph and the projection graph is high, and thus the properties of the (dynamic) projection graph can be inferred from the properties of the (slower changing) social graph. Furthermore, we demonstrate with two application scenarios on large-scale social networks the usability of the projection graph in designing social search applications and unstructured P2P overlays.
In other studies, such as @cite_3 , peers are organized into social P2P networks based on similar preferences, interests or knowledge of their users, to improve search by utilizing peers trusted or relevant to the search. Similarly, in @cite_6 a social-based overlay for unstructured P2P networks is outlined, that enables peers to find and establish ties with other peers if their owners have common interest in specific types of content, thus improving search and reducing overlay construction overhead. @cite_28 , P2P social networks self-organize based on the concept of distributed neuron-like agents and search stimulus between peers, to facilitate improved resource sharing and search. In such systems, the peers form edges over similar preferences of their owners or search requests (i.e, @math edges). Thus, they implicitly use the @math model to organize peers into a P2P social network.
@cite_28 @cite_6 @cite_3
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Peer-to-peer (P2P) systems provide a new solution to distributed information and resource sharing because of its outstanding properties in decentralization, dynamics, flexibility, autonomy, and cooperation, summarized as DDFAC in this paper. After a detailed analysis of the current P2P literature, this paper suggests to better exploit peer social relationships and peer autonomy to achieve efficient P2P structure design. Accordingly, this paper proposes Self-organizing peer-to-peer social networks (SoPPSoNs) to self-organize distributed peers in a decentralized way, in which neuron-like agents following extended Hebbian rules found in the brain activity represent peers to discover useful peer connections. The self-organized networks capture social associations of peers in resource sharing, and hence are called P2P social networks. SoPPSoNs have improved search speed and success rate as peer social networks are correctly formed. This has been verified through tests on real data collected from the Gnutella system. Analysis on the Gnutella data has verified that social associations of peers in reality are directed, asymmetric and weighted, validating the design of SoPPSoN. The tests presented in this paper have also evaluated the scalability of SoPPSoN, its performance under varied initial network connectivity and the effects of different learning rules. The widespread use of peer-to-peer (P2P) systems has made multimedia content sharing more efficient. Users in a P2P network can query and download objects based on their preference for specific types of multimedia content. However, most P2P systems only construct the overlay architecture according to physical network constraints and do not take user preferences into account. In this paper, we investigate a social-based overlay that can cluster peers that have similar preferences. To construct a semantic social-based overlay, we model a quantifiable measure of similarity between peers so that those with a higher degree of similarity can be connected by shorter paths. Hence, peers can locate objects of interest from their overlay neighbors, i.e., peers who have common interests. In addition, we propose an overlay adaptation algorithm that allows the overlay to adapt to P2P churn and preference changes in a distributed manner. We use simulations and a real database called Audioscrobbler, which tracks users' listening habits, to evaluate the proposed social-based overlay. The results show that social-based overlay adaptation enables users to locate content of interest with a higher success ratio and with less message overhead.