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Significant Node Identification in Social Networks

  • Chi-Yao Tseng
  • Ming-Syan Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7104)

Abstract

Given a social network, identifying significant nodes from the network is highly desirable in many applications. In different networks formed by diverse kinds of social connections, the definitions of what are significant nodes differ with circumstances. In the literature, most previous works generally focus on expertise finding in specific social networks. In this paper, we aim to propose a general node ranking model that can be adopted to satisfy a variety of service demands. We devise an unsupervised learning method that produces the ranking list of top-k significant nodes. The characteristic of this method is that it can generate different ranking lists when diverse sets of features are considered. To demonstrate the real application of the proposed method, we design the system DblpNET that is an author ranking system based on the co-author network of DBLP computer science bibliography. We discuss further extensions and evaluate DblpNET empirically on the public DBLP dataset. The evaluation results show that the proposed method can effectively apply to real-world applications.

Keywords

Citation Count Ranking List Component Size Ranking Algorithm Closeness Centrality 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Zhang, J., Ackerman, M.S., Adamic, L.: Expertise Networks in Online Communities: Structure and Algorithms. In: Proc. of the 16th International Conference on World Wide Web (WWW), pp. 221–230 (2007)Google Scholar
  2. 2.
    Noll, M.G., Au Yeung, C., Gibbins, N., Meinel, C., Shadbolt, N.: Telling Experts from Spammers: Expertise Ranking in Folksonomies. In: Proc. of the 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), pp. 612–619 (2009)Google Scholar
  3. 3.
    Balog, K., Azzopardi, L., de Rijke, M.: Formal Models for Expert Finding in Enterprise Corpora. In: Proc. of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), pp. 43–50 (2006)Google Scholar
  4. 4.
    Dom, B., Eiron, I., Cozzi, A., Zhang, Y.: Graph-Based Ranking Algorithms for Email Expertise Analysis. In: Proc. of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, pp. 42–48 (2003)Google Scholar
  5. 5.
    Zhang, J., Tang, J., Li, J.: Expert Finding in a Social Network. In: Kotagiri, R., Radha Krishna, P., Mohania, M., Nantajeewarawat, E. (eds.) DASFAA 2007. LNCS, vol. 4443, pp. 1066–1069. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  6. 6.
    Ley, M.: The DBLP (Digital Bibliography and Library Project) Computer Science Bibliography, http://www.informatik.uni-trier.de/~ley/db/
  7. 7.
    Liu, X., Bollen, J., Nelson, M.L., de Sompel, H.V.: Co-authorship Networks in the Digital Library Research Community. In: Information Processing and Management, pp. 1462–1480 (2005)Google Scholar
  8. 8.
    Nascimento, M.A., Sander, J., Pound, J.: Analysis of SIGMOD’s Co-Authorship Graph. ACM SIGMOD Record, 8–10 (2003)Google Scholar
  9. 9.
    Newman, M.E.J.: Coauthorship Networks and Patterns of Scientific Collaboration. Proc. of the National Academy of Sciences, 5200–5205 (2004)Google Scholar
  10. 10.
    Zaïane, O.R., Chen, J., Goebel, R.: Mining Research Communities in Bibliographical Data. In: Zhang, H., Spiliopoulou, M., Mobasher, B., Giles, C.L., McCallum, A., Nasraoui, O., Srivastava, J., Yen, J. (eds.) WebKDD 2007. LNCS, vol. 5439, pp. 59–76. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  11. 11.
    Zhou, D., Orshanskiy, S.A., Zha, H., Giles, C.L.: Co-Ranking Authors and Documents in a Heterogeneous Network. In: Proc. of the 7th IEEE International Conference on Data Mining (ICDM), pp. 739–744 (2007)Google Scholar
  12. 12.
    Tseng, C.Y., Chen, M.S.: Significant Nodes Identification in the Co-author Network of DBLP, http://cytseng.no-ip.org/dblpnet.php
  13. 13.
    Newman, M.E.J.: The Structure of Scientific Collaboration Networks. Proc. of the National Academy of Sciences, 404–409 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Chi-Yao Tseng
    • 1
  • Ming-Syan Chen
    • 1
  1. 1.Research Center for Information Technology InnovationAcademia SinicaTaipeiTaiwan, ROC

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