The dependencies between power law parameters such as in-degree and PageRank, can be characterized by the so-called angular measure, a notion used in extreme value theory to describe the dependency between very large values of coordinates of a random vector. Basing on an analytical stochastic model, we argue that the angular measure for in-degree and personalized PageRank is concentrated in two points. This corresponds to the two main factors for high ranking: large in-degree and a high rank of one of the ancestors. Furthermore, we can formally establish the relative importance of these two factors. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Litvak, N., Scheinhardt, W., Volkovich, Y., & Zwart, B. (2009). Characterization of tail dependence for in-degree and pageRank. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5427 LNCS, pp. 90–103). https://doi.org/10.1007/978-3-540-95995-3_8
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