文章基本信息

标题：Applying Hebbian Theory to Enhance Search Performance in Unstructured Social-Like Peer-to-Peer Networks
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作者：Huang, Chester S.J. ; Yang, Stephen J.H. ; Su, Addison Y.S. 等
期刊名称：ETRI Journal
印刷版ISSN：1225-6463
电子版ISSN：2233-7326
出版年度：2012
卷号：34
期号：4
页码：591-601
DOI：10.4218/etrij.12.0111.0588
语种：English
出版社：Electronics and Telecommunications Research Institute
摘要：Unstructured peer-to-peer (p2p) networks usually employ flooding search algorithms to locate resources. However, these algorithms often require a large storage overhead or generate massive network traffic. To address this issue, previous researchers explored the possibility of building efficient p2p networks by clustering peers into communities based on their social relationships, creating social-like p2p networks. This study proposes a social relationship p2p network that uses a measure based on Hebbian theory to create a social relation weight. The contribution of the study is twofold. First, using the social relation weight, the query peer stores and searches for the appropriate response peers in social-like p2p networks. Second, this study designs a novel knowledge index mechanism that dynamically adapts social relationship p2p networks. The results show that the proposed social relationship p2p network improves search performance significantly, compared with existing approaches.
关键词：P2P query routing;social networks;semantic search