Efficient Content Location Using Semantic Small World in Peer-to-Peer Networks
Abstract: Locating content in unstructured
peer-to-peer networks is a challenging problem. This paper presents a novel
semantic small world resource search mechanism to address the problem. By using
vector space model to compute the semantic relevance and applying small world
properties such as low average hop distance and high clustering coefficient to
construct a cluster overlay. In semantic small world system, the search
mechanism is divided into two parts, searching at cluster and outside cluster
through inner link and short link, so that it can achieve the incremental
research. It significantly reduces the average path length and query cost.
Meanwhile, the simulation results show that semantic small world scheme
outperforms K-random walks and flooding scheme than higher query hit rate and
lower query latency.
Author: Yong Chen, Wei-zhong
Xiao, Huan-lin Liu, Long-zhao Sun
Journal Code: jptkomputergg130042
