The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Using PageRank to Characterize Web Structure
COCOON '02 Proceedings of the 8th Annual International Conference on Computing and Combinatorics
The webgraph framework I: compression techniques
Proceedings of the 13th international conference on World Wide Web
Distances in random graphs with finite variance degrees
Random Structures & Algorithms
Graph mining: Laws, generators, and algorithms
ACM Computing Surveys (CSUR)
Measuring extremal dependencies in web graphs
Proceedings of the 17th international conference on World Wide Web
Approximating PageRank from In-Degree
Algorithms and Models for the Web-Graph
Probabilistic Relation between In-Degree and PageRank
Algorithms and Models for the Web-Graph
Determining factors behind the PageRank log-log plot
WAW'07 Proceedings of the 5th international conference on Algorithms and models for the web-graph
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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.