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Mining Temporally Changing Web Usage Graphs

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Advances in Web Mining and Web Usage Analysis (WebKDD 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3932))

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Abstract

Web mining has been explored to a vast degree and different techniques have been proposed for a variety of applications that include Web Search, Web Classification, Web Personalization etc. Most research on Web mining has been from a ‘data-centric’ point of view. The focus has been primarily on developing measures and applications based on data collected from content, structure and usage of Web until a particular time instance. In this project we examine another dimension of Web Mining, namely temporal dimension. Web data has been evolving over time, reflecting the ongoing trends. These changes in data in the temporal dimension reveal new kind of information. This information has not captured the attention of the Web mining research community to a large extent. In this paper, we highlight the significance of studying the evolving nature of the Web graphs. We have classified the approach to such problems at three levels of analysis: single node, sub-graphs and whole graphs. We provide a framework to approach problems in this kind of analysis and identify interesting problems at each level. Our experiments verify the significance of such an analysis and also point to future directions in this area. The approach we take is generic and can be applied to other domains, where data can be modeled as a graph, such as network intrusion detection or social networks.

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Author information

Authors and Affiliations

  1. Department of Computer Science University of Minnesota, Minneapolis, MN, 55414, USA

    Prasanna Desikan & Jaideep Srivastava

Authors
  1. Prasanna Desikan
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  2. Jaideep Srivastava
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Editor information

Editors and Affiliations

  1. Center for Web Intelligence School of Computing, DePaul University, Chicago, Illinois, USA

    Bamshad Mobasher

  2. Speed School of Engineering, Department of Computer Engineering & Computer Science, University of Louisville, KY 40292, Louisville, USA

    Olfa Nasraoui

  3. College of Architecture and Urban Planning, Tongji University, 1239 Siping Road, 200092, Shanghai, P.R. China

    Bing Liu

  4. Data Miners Inc., 77 North Washington Street, MA 02114, Boston, USA

    Brij Masand

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© 2006 Springer-Verlag Berlin Heidelberg

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Desikan, P., Srivastava, J. (2006). Mining Temporally Changing Web Usage Graphs. In: Mobasher, B., Nasraoui, O., Liu, B., Masand, B. (eds) Advances in Web Mining and Web Usage Analysis. WebKDD 2004. Lecture Notes in Computer Science(), vol 3932. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11899402_1

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  • DOI: https://doi.org/10.1007/11899402_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-47127-1

  • Online ISBN: 978-3-540-47128-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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