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Trend detection through temporal link analysis

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  • Einat Amitay
  • David Carmel
  • Michael Herscovici
  • Ronny Lempel
  • Aya Soffer

Abstract

Although time has been recognized as an important dimension in the co‐citation literature, to date it has not been incorporated into the analogous process of link analysis on the Web. In this paper, we discuss several aspects and uses of the time dimension in the context of Web information retrieval. We describe the ideal case—where search engines track and store temporal data for each of the pages in their repository, assigning timestamps to the hyperlinks embedded within the pages. We introduce several applications which benefit from the availability of such timestamps. To demonstrate our claims, we use a somewhat simplistic approach, which dates links by approximating the age of the page's content. We show that by using this crude measure alone it is possible to detect and expose significant events and trends. We predict that by using more robust methods for tracking modifications in the content of pages, search engines will be able to provide results that are more timely and better reflect current real‐life trends than those they provide today.

Suggested Citation

  • Einat Amitay & David Carmel & Michael Herscovici & Ronny Lempel & Aya Soffer, 2004. "Trend detection through temporal link analysis," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 55(14), pages 1270-1281, December.
    • Handle: RePEc:bla:jamist:v:55:y:2004:i:14:p:1270-1281
    DOI: 10.1002/asi.20082
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    Cited by:

    1. Guan, Jiancheng & Liu, Na, 2015. "Invention profiles and uneven growth in the field of emerging nano-energy," Energy Policy, Elsevier, vol. 76(C), pages 146-157.
    2. Jeong, Do-Heon & Song, Min, 2014. "Time gap analysis by the topic model-based temporal technique," Journal of Informetrics, Elsevier, vol. 8(3), pages 776-790.
    3. Xiaoguang Wang & Qikai Cheng & Wei Lu, 2014. "Analyzing evolution of research topics with NEViewer: a new method based on dynamic co-word networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1253-1271, November.
    4. Rousseau, Ronald & Hu, Xiaojun, 2013. "Two time series, their meaning and some applications," Journal of Informetrics, Elsevier, vol. 7(3), pages 603-610.

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