BuzzRank ... and the Trend is Your Friend

• Klaus Berberich, Max-Planck-Institut fur Informatik, Germany
• Srikanta Bedathur, Max-Planck-Institut fur Informatik, Germany
• Michalis Vazirgiannis, Athens University of Economics and Business, Greece
• Gerhard Weikum, Max-Planck-Institut fur Informatik, Germany

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Track: Posters

Ranking methods like PageRank assess the importance of Web pages based on the current state of the rapidly evolving Web graph. The dynamics of the resulting importance scores, however, have not been considered yet, although they provide the key to an understanding of the Zeitgeist on the Web. This paper proposes the BuzzRank method that quantifies trends in time series of importance scores and is based on a relevant growth model of importance scores. We experimentally demonstrate the usefulness of BuzzRank on a bibliographic dataset.

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