Chord: A scalable peer-to-peer lookup service for internet applications
Proceedings of the 2001 conference on Applications, technologies, architectures, and protocols for computer communications
pSearch: information retrieval in structured overlays
ACM SIGCOMM Computer Communication Review
SETS: search enhanced by topic segmentation
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Proceedings of the twenty-second annual symposium on Principles of distributed computing
Creating social networks to improve peer-to-peer networking
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
On the topologies formed by selfish peers
Proceedings of the twenty-fifth annual ACM symposium on Principles of distributed computing
Proceedings of the 1st ACM SIGOPS/EuroSys European Conference on Computer Systems 2006
Optimizing Peer Relationships in a Super-Peer Network
ICDCS '07 Proceedings of the 27th International Conference on Distributed Computing Systems
Measurement and analysis of online social networks
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Modelling Real P2P Networks: The Effect of Altruism
P2P '07 Proceedings of the Seventh IEEE International Conference on Peer-to-Peer Computing
Seeking stable clusters in the blogosphere
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
DESENT: decentralized and distributed semantic overlay generation in P2P networks
IEEE Journal on Selected Areas in Communications
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In many large-scale content sharing applications, participants or peers are grouped together forming clusters based on their content or interests. In this paper, we deal with the maintenance of such clusters in the presence of updates. We model the evolution of the system as a strategic game, where peers determine their cluster membership based on a utility function of the query recall. Peers are guided either by selfish or altruistic motives: selfish peers aim at improving the recall of their own queries, whereas altruistic peers aim at improving the recall of the queries of other peers. We study the evolution of such clusters both theoretically and experimentally under a variety of conditions. We show that, in general, local decisions made independently by each peer enable the system to adapt to changes and maintain the overall recall of the query workload.