A New Algorithm for Personalized Recommendations in Community Networks

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

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.

Keywords

Clustering Graph theory Hierarchical tree Social network 

Notes

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.

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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.China Information Technology Security Evaluation CenterBeijingChina
  2. 2.School of Electronic and Information EngineeringBeijing Jiaotong UniversityBeijingChina
  3. 3.Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of EducationBeijing Jiaotong UniversityBeijingChina

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