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Mining Research Communities in Bibliographical Data

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Advances in Web Mining and Web Usage Analysis (SNAKDD 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5439))

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Abstract

Extracting information from very large collections of structured, semi-structured or even unstructured data can be a considerable challenge when much of the hidden information is implicit within relationships among entities in the data. Social networks are such data collections in which relationships play a vital role in the knowledge these networks can convey. A bibliographic database is an essential tool for the research community, yet finding and making use of relationships comprised within such a social network is difficult. In this paper we introduce DBconnect, a prototype that exploits the social network coded within the DBLP database by drawing on a new random walk approach to reveal interesting knowledge about the research community and even recommend collaborations.

This work is based on an earlier work: DBconnect: mining research community on DBLP data, in Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis, COPYRIGHT ACM, 2007, http://portal.acm.org/ citation.cfm?doid=1348549.1348558

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Author information

Authors and Affiliations

  1. University of Alberta, Canada

    Osmar R. Zaïane, Jiyang Chen & Randy Goebel

Authors
  1. Osmar R. Zaïane
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  2. Jiyang Chen
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  3. Randy Goebel
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Editor information

Editors and Affiliations

  1. One Microsoft Way, WA 98052, Redmond, USA

    Haizheng Zhang

  2. Faculty of Computer Science, Otto von Guericke University Magdeburg, 39016, Magdeburg, Germany

    Myra Spiliopoulou

  3. School of Computing Science, Telecommunications Information Systems, DePaul University, IL 60604, Chicago, USA

    Bamshad Mobasher

  4. College of Information Science and Technology, Pennsylvania State University, PA 16802, University Park, USA

    C. Lee Giles

  5. Department of Computer Science, University of Massachusetts, MA 01003, Amherst, USA

    Andrew McCallum

  6. Computer Engineering and Computer Science Department, University of Louisville, KY 40292, Louisville, USA

    Olfa Nasraoui

  7. Department of Computer Science and Engineering, University of Minnesota, MN 55455, Minneapolis, USA

    Jaideep Srivastava

  8. College of Information Sciences and Technology, The Pennsylvania State University, PA 16802, Unversity Park, USA

    John Yen

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Zaïane, O.R., Chen, J., Goebel, R. (2009). Mining Research Communities in Bibliographical Data. In: Zhang, H., et al. Advances in Web Mining and Web Usage Analysis. SNAKDD 2007. Lecture Notes in Computer Science(), vol 5439. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00528-2_4

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  • DOI: https://doi.org/10.1007/978-3-642-00528-2_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-00527-5

  • Online ISBN: 978-3-642-00528-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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