Open AccessApplying Hebbian Theory to Enhance Search Performance in Unstructured Social‐Like Peer‐to‐Peer Networks
Author(s) -
Huang Chester S.J.,
Yang Stephen J.H.,
Su Addison Y.S.
Publication year - 2012
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.12.0111.0588
Subject(s) - computer science , social network (sociolinguistics) , peer to peer , relation (database) , cluster analysis , flooding (psychology) , overhead (engineering) , social network analysis , hebbian theory , distributed computing , artificial intelligence , data mining , social media , world wide web , artificial neural network , psychology , psychotherapist , operating system
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.