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A New Algorithm for Personalized Recommendations in Community Networks

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Advanced Technologies, Embedded and Multimedia for Human-centric Computing

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 260))

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Abstract

In a graph theory model, clustering is the process of division of vertices in groups, with a higher density of edges in groups than among them. In this paper, we introduce a new clustering algorithm for detecting such groups; we use it to analyze some classic social networks. The new algorithm has two distinguished features: non-binary hierarchical tree and the feature of overlapping clustering. A non-binary hierarchical tree is much smaller than the binary-trees constructed by most traditional algorithms; it clearly highlights meaningful clusters which significantly reduce further manual efforts for cluster selections. The present algorithm is tested by several bench mark data sets for which the community structure was known in advance and the results indicate that it is a sensitive and accurate algorithm for extracting community structure from social networks.

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Acknowledgments

This work has been supported by the National Natural Science Foundation of China under Grant 61172072, 61271308, the Beijing Natural Science Foundation under Grant 4112045, the Research Fund for the Doctoral Program of Higher Education of China under Grant W11C100030, the Beijing Science and Technology Program under Grant Z121100000312024.

Author information

Authors and Affiliations

  1. China Information Technology Security Evaluation Center, Beijing, China

    Xin Zhou

  2. School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, 100044, China

    XinXiang Xing & Yun Liu

  3. Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education, Beijing Jiaotong University, Beijing, 100044, China

    XinXiang Xing & Yun Liu

Authors
  1. Xin Zhou
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  2. XinXiang Xing
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  3. Yun Liu
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Corresponding author

Correspondence to Yun Liu .

Editor information

Editors and Affiliations

  1. ES Department, National Cheng Kung University, Tainan City, Taiwan

    Yueh-Min Huang

  2. Engineering, National Ilan University Inst. Computer Science & Information, I-Lan, Taiwan

    Han-Chieh Chao

  3. Dept of Computer Sci. and Inf. Bao-Shan Campus, National Changhua University of Education, Changhua City, Taiwan

    Der-Jiunn Deng

  4. Department of Computer Science and Engineering, Seoul University of Science & and Technology (SeoulTech), Seoul, Korea, Republic of (South Korea)

    James J. (Jong Hyuk) Park

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Zhou, X., Xing, X., Liu, Y. (2014). A New Algorithm for Personalized Recommendations in Community Networks. In: Huang, YM., Chao, HC., Deng, DJ., Park, J. (eds) Advanced Technologies, Embedded and Multimedia for Human-centric Computing. Lecture Notes in Electrical Engineering, vol 260. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7262-5_86

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  • DOI: https://doi.org/10.1007/978-94-007-7262-5_86

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  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-007-7261-8

  • Online ISBN: 978-94-007-7262-5

  • eBook Packages: EngineeringEngineering (R0)

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