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Exploring Community Structures by Comparing Group Characteristics

  • Guanling LeeEmail author
  • Chia-Jung Chang
  • Sheng-Lung Peng
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 260)

Abstract

In recent years, more and more researchers devoted to identifying community structure in social networks. The characteristics of the social network are analyzed by clustering the social network users according to user’s relationships. However, the users of current popular social networks such as LiveJournal and Flickr, can join to or create the communities according to their interests. Instead of grouping the users according to the cluster strategies which are wildly used in previous works, the purpose of the paper is to explore the structures and characteristics of the social networks according to the community the users actually joined. Moreover, we experiment on four real datasets, LiveJournal, Flickr, Orkut and Youtube, to analyze the characteristics hidden behind the social networks.

Keywords

Social network Community structure Cluster 

Notes

Acknowledgments

This work was partially supported by the National Science Council of Taiwan, under contracts NSC 101-2221-E-259-002 and NSC 101-2221-E-259-004.

References

  1. 1.
    Chen W, Wang Y, Yang S (2009) Efficient Influence maximization in social networks. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining, pp 199–208Google Scholar
  2. 2.
    Kempe D, Kleinberg J, Tardos E (2003) Maximizing the spread of influence through a social network. In: Proceedings of SIGKDD 2003, pp 3–9Google Scholar
  3. 3.
    Goyal A, Bonchi F, Lakshmanan L (2008) Discovering leaders from community actions. In: Proceedings of ACM conference on Information and knowledge management, pp 3–7Google Scholar
  4. 4.
    Biryukov M (2008) Co-author network analysis in DBLP: classifying personal names. Springer, Berlin Heidelberg, pp 403–407Google Scholar
  5. 5.
    Lancichinetti A, Fortunato S, Kertesz J (2009) Detecting the overlapping and hierarchical community structure of complex networks. Physics 11(3):4–14 (arxiv.org)Google Scholar
  6. 6.
    Backstrom L, Huttenlocher D, Kleinberg J, Lan X (2006) Group formation in large social networks: membership, growth, and evolution. In: Proceedings of ACM KDD 2006, pp 44–53Google Scholar
  7. 7.
    Licamele L, Getoor L (2006) Social capital in friendship-event networks. In: Proceedings of the sixth IEEE international conference on data mining, ICDM 2006, pp 1–12Google Scholar
  8. 8.

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Guanling Lee
    • 1
    Email author
  • Chia-Jung Chang
    • 1
  • Sheng-Lung Peng
    • 1
  1. 1.Department of Computer Science and Information EngineeringNational Dong Hwa UniversityHualienTaiwan, Republic of China

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