Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/8944
Title: Self-organizing peer-to-peer social networks
Authors: Wang, F
Sun, Y
Keywords: Peer-to-peer;Agents;Hebbian learning;Self-organizing;Social networks;Algorithms;Design
Issue Date: 2008
Publisher: Wiley
Citation: Computational Intelligence, 24(3), 213 - 233, 2008
Abstract: 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.
Description: This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2008 The Authors.
URI: http://onlinelibrary.wiley.com/doi/10.1111/j.1467-8640.2008.00328.x/abstract
http://bura.brunel.ac.uk/handle/2438/8944
DOI: http://dx.doi.org/10.1111/j.1467-8640.2008.00328.x
ISSN: 0824-7935
Appears in Collections:Computer Science
Dept of Computer Science Research Papers

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