Self-Organizing Peer-to-Peer Social Networks

Peer-to-Peer (P2P) systems provide a new solution to distributed information and resource sharing because of its outstanding properties in decentralisation, dynamics, flexibility, autonomy and cooperation, summarised 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 in order to achieve efficient P2P structure design. Accordingly, this paper proposes Self-organising Peer-to-Peer Social Networks (SoPPSoNs) to self-organise distributed peers in a decentralised way, in which neuron-like agents following extended Hebbian rules found in the brain activity represent peers to discover useful peer connections. The self-organised 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. Categories and subject descriptors: C.2.1 [Computer-Communication Networks]: Network Architecture and Design---network topology; C.2.4 [Computer-Communication Networks]: Distributed Systems---distributed applications; I.2.6 [Artificial Intelligence]: Learning---connectionism and neural nets; I.2.11 [Artificial Intelligence]: Distributed Artificial Intelligence---intelligent agents General Terms: Algorithms, Design Additional