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Social Peer-to-Peer for Resource Discovery

Liu, Lu, Antonopoulos, Nick and Makin, Stephen (2007) Social Peer-to-Peer for Resource Discovery In: 15th EUROMICRO International Conference on Parallel, Distributed and Network-Based Processing (PDP'07).


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For resource discovery in social networks, people can directly contact some acquaintances that have knowledge about the resources they are looking for. However, in current peer-to-peer networks, peer nodes lack capabilities similar to social networks, making it difficult to route queries efficiently. In this paper, we present a social-like system (Social-P2P) for resource discovery by mimicking human behaviours in social networks. Different from most informed search algorithms, peer nodes learn knowledge from the results of previous searches and no additional overhead is required to obtain extra information from neighbouring nodes. Unlike community-based P2P information sharing systems, we do not intend to create and maintain peer groups or communities consciously. Peer nodes with the same interests will be highly connected to each other spontaneously. Social-P2P has been simulated in a dynamic environment. From the simulation results and analysis, Social-P2P achieved better performance than current methods.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
Liu, Lu
Makin, Stephen
Date : 7 February 2007
DOI : 10.1109/PDP.2007.76
Additional Information : 15th EUROMICRO International Conference on Parallel, Distributed and Network-Based Processing (PDP'07), 459-466.© 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Depositing User : Mr Adam Field
Date Deposited : 27 May 2010 14:46
Last Modified : 31 Oct 2017 14:03

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