Cooperation through self-similar social networks
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Modeling random walk search algorithms in unstructured P2P networks with social information
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
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Due to the dynamic nature of P2P systems, it is impossible to keep an accurate history of the transactions that take place while avoiding security attacks such as whitewashing and collusion, and abuse such as freeriding. This is why it is important to develop a mechanism that both rewards cooperative peers and punishes misbehaving peers. Modelling P2P networks as social structures can allow incentive mechanisms to be developed that prevent the negative behaviors mentioned. In a social structure, peers make and receive payments for services provided to and from each other. In this paper we extend a social network algorithm to include the transfer of credit between peers to reduce the path length in queries. We also develop a selection strategy that involves different aspects of peer interactions in a P2P network and a credit transfer mechanism that helps to dis-incent misbehaving peers by taking away credits that they have with good peers and transferring them to more cooperative ones. The simulation results show that our algorithm is effective in reducing the amount of debt between peers, meaning that peers become more cooperative, and shortening the average path length to a satisfied query, while increasing delivery ratio.