The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
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
Impact of search engines on page popularity
Proceedings of the 13th international conference on World Wide Web
Incremental page rank computation on evolving graphs
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
Page quality: in search of an unbiased web ranking
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
BuzzRank … and the trend is your friend
Proceedings of the 15th international conference on World Wide Web
Comparing apples and oranges: normalized pagerank for evolving graphs
Proceedings of the 16th international conference on World Wide Web
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Within publication digital collections, citation analysis and publication score assignment are commonly used (i) to evaluate the impact of publications (and scientific collections, e.g., journals and conferences), and (ii) to order digital collection search outputs, e.g., Google Scholar. The popular citation-based web page (and, thus, publication) score measure PageRank is criticized for (a) computing only the current (and, thus, time-independent) publication scores, and (b) not taking into account the fa ct that citation graphs continuously evolve. Thus, the use ofPageRank as is results in penalizing recent publications that have not yet developed enough popularity to receive citations. In order to overcome this inherent bias of PageRank and other citation-based popularity measures, Cho et. al. defined Page Quality for a webpage as its popularity after large numbers ofweb users become aware ofit. Page Quality is based on the assumption that popularity evolves over time. In this paper, we (i) experimentally validate that PageRank scores ofpublications, as they change over time, follow the logistic growth model that often arises in the context of population growth, (ii) model one aspect of researchers' citation behavior in technology-driven fields (such as computer science) where authors tend not to cite old publications, (iii) argue and empirically verify that publication popularity, unlike web page popularity, has two distinct phases, namely, the popularity growth phase and the popularity decay phase, and (iv) extend the popularity growth model developed by Cho et. al. to capture the popularity decay phase. All of our claims are empirically verified using the ACM SIGMOD Anthology digital collection.