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Research on social network discovery algorithm in pervasive sensing environment
Author(s) -
Gao Kun,
Zhu Yiwei,
Gong Songjie,
Tan Hengsong,
Zhou Guangyu
Publication year - 2016
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.3815
Subject(s) - scalability , partition (number theory) , computer science , index (typography) , data mining , partition problem , social network (sociolinguistics) , network partition , algorithm , artificial intelligence , distributed computing , mathematics , social media , combinatorics , database , world wide web
Summary Considering the real social community network partition approach regardless of the directed and weighted characteristic, we propose a novel algorithm in pervasive sensing environment. The proposed SDOR algorithm is based on the definition of nodes optimal route, community likeness index, community discrete degree index and so on parameters to achieve the sensible partition for directed weighted social community network. We conduct some different types of experiments to verify the scalability, accuracy, and validity of the proposed algorithm. The experimental consequences demonstrate that the accuracy of the algorithm is about 90%, higher than existing models about 10%. In addition, the algorithm has good effectiveness and scalability in other different kinds of network. Copyright © 2016 John Wiley & Sons, Ltd.