PageRank on Directed Complex Networks

Mariana Olvero-Cravioto, UNC

Friday, September 27, 2019 - 3:30pm

During this talk I will discuss the typical behavior of Google’s PageRank algorithm on large directed random graphs. This analysis is based on a result that establishes the convergence in the Kantorovich-Rubinstein metric of the rank of a randomly chosen vertex to a random variable that can be written in terms of the attracting endogenous solution to a branching distributional fixed-point equation. We then show how, by analyzing the asymptotic behavior of this solution, we can identify which vertices are most likely to attain high ranks. Our results also show how the typical behavior of PageRank changes depending on how the in-degrees, out-degrees and personalization values of vertices in the underlying graph are related to each other, in particular, on their dependence structure. 

Seminars generally take place in 116 Old Chemistry Building on Fridays from 3:30 - 4:30 pm. For additional information contact: karen.whitesell@duke.edu or phone 919-684-8029. Sorry, but we do not have reprints available. Please feel free to contact the authors by email for follow-up information, articles, etc. Reception following seminar in 203B Old Chemistry.

Old Chemistry 116

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