A self-organized semantic clustering approach for super-peer networks

Authors:
Baiyou Qiao;Guoren Wang;Kexin Xie
Affiliations:
School of Information Science and Engineering, Northeastern University, Shenyang, China;School of Information Science and Engineering, Northeastern University, Shenyang, China;School of Information Science and Engineering, Northeastern University, Shenyang, China
Venue:
WISE'06 Proceedings of the 7th international conference on Web Information Systems
Year:
2006

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Abstract

Partitioning a P2P network into distinct semantic clusters can efficiently increase the efficiency of searching and enhance scalability of the network. In this paper, two semantic-based self-organized algorithms aimed at taxonomy hierarchy semantic space are proposed, which can dynamically partition the network into distinct semantic clusters according to network load, with semantic relationship among data within a cluster and load balance among clusters all well maintained. The experiment indicates good performance and scalability of these two clustering algorithms.