Randomized algorithms
Balls and bins models with feedback
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
Using PageRank to Characterize Web Structure
COCOON '02 Proceedings of the 8th Annual International Conference on Computing and Combinatorics
Random Structures & Algorithms
Stochastic models for the Web graph
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Impact of search engines on page popularity
Proceedings of the 13th 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
The influence of search engines on preferential attachment
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
Shuffling a stacked deck: the case for partially randomized ranking of search engine results
VLDB '05 Proceedings of the 31st international conference on Very large data bases
The web as a graph: measurements, models, and methods
COCOON'99 Proceedings of the 5th annual international conference on Computing and combinatorics
First to market is not everything: an analysis of preferential attachment with fitness
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
Algorithms and incentives for robust ranking
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
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The link structure of the Web can be viewed as a massive graph. The preferential attachment model and its variants are well-known random graph models that help explain the evolution of the web graph. However, those models assign more links to older pages without reference to the quality of web pages, which does not capture the real-world evolution of the web graph and renders the models inappropriate for studying the popularity evolution of new pages.We extend the preferential attachment model with page quality, where the probability of a page getting new links depends not only on its current degree but also on its quality. We study the distribution of degrees among different quality values, and prove that under discrete quality distributions, the degree sequence still follows a power law distribution. Then we use the model to study the evolution of page popularity. We show that for pages with the same quality, the older pages are more popular; if a younger page is better than an older page, then eventually the younger-and-better page will become more popular. We also use the model to study a randomized ranking scheme proposed earlier [18] and show that it accelerates popularity evolution of new pages.