What's new on the web?: the evolution of the web from a search engine perspective
Proceedings of the 13th international conference on World Wide Web
Sic transit gloria telae: towards an understanding of the web's decay
Proceedings of the 13th international conference on World Wide Web
A large-scale study of the evolution of web pages
Software—Practice & Experience - Special issue: Web technologies
On the temporal dimension of search
Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters
Trend detection through temporal link analysis
Journal of the American Society for Information Science and Technology - Special issue: Webometrics
Page quality: in search of an unbiased web ranking
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
The database research group at the Max-Planck Institute for Informatics
ACM SIGMOD Record
Comparing apples and oranges: normalized pagerank for evolving graphs
Proceedings of the 16th international conference on World Wide Web
On popularity quality: growth and decay phases of publication popularities
IIT'09 Proceedings of the 6th international conference on Innovations in information technology
Freshness matters: in flowers, food, and web authority
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Recency-sensitive model of web page authority
Proceedings of the 21st ACM international conference on Information and knowledge management
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
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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.