A Survey on PageRank Computing

By Pavel Berkhin. Internet Mathematics, 2005

496 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

PageRank beyond the Web (2014, methodology): “The essence of the argument is in the references Pretto (2002), Gleich, Zhukov, and Berkhin (2004), and Del Corso, Gulĺı, and Romani (2005).”
Unsupervised modeling of object categories using link analysis techniques (2008, methodology): “This is similar to the behavior observed in other applications of the link analysis techniques [3].”
Random walks and diffusion on networks (2016, background): “An alternative choice is a “personalized PageRank” [23, 288–291, 301–304], in which the preference vector is localized around one node or a small number of nodes (which can be helpful for applications to community detection [68]).”
Expertise networks in online communities: structure and algorithms (2007, background): “There are various versions of PageRank or similar measures; for an overview, see [4, 6].”
Adversarial Web Search (2011, unknown): “For a survey specifically on how to compute PageRank values efficiently, see Berkhin [23].”
Financial Network Systemic Risk Contributions (2015, unknown): “For more details, see Berkhin (2005).”
Reinforcement Ranking (2013, methodology): “Second, no special treatment is required for dangling pages, which has generally been considered tricky for Page Rank [13, 6].”