Google's PageRank and Beyond

By Jim Hendler. 2006

1,568 citations recorded by the source corpus.

Why is Google so good at what it does? There ate a variety of reasons, but the fundamental thing that distinguishes Google and has put them so far ahead of other search engines is their patented PageRank concept. PageRank has revolutionized Web search to the extent that it has been charged in Federal Court with driving the direction of commerce on the Internet. Many mathematicians are therefore surprised when they learn that a technology of such consequence is predicated on the same mathematics that is available to undergraduate students. This talk will survey some of these concepts.

Subfields: Information Systems

View Paper: https://www.semanticscholar.org/paper/b9ff3c9218308c8afa71c5bcdaf3bc72afb87b65

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In Information Systems

Citations in the corpus

Reinforcement Ranking (2013, background): “This confir ms Google’s report that historical update based power iterati on does not improve the accuracy for Page Rank [25].”
Computing Personalized PageRank Quickly by Exploiting Graph Structures (2014, background): “According to [32], it should be less than 10 − 9 .”
Portfolio: finding relevant functions and their usage (2011, background): “PageRank is widely described in literature, so here we give its concise mathematical explanation as it is related to Portfolio [2, 19].”
Guidelines for including the grey literature and conducting multivocal literature reviews in software engineering (2017, background): “Obviously, in such cases, one needs to rely on the search engine page rank algorithm [91] and choose to investigate only a suitable number of hits.”
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]).”
Naïve Learning in Social Networks and the Wisdom of Crowds (2010, background): “To each interaction matrix T(n) corresponds a unique left eigenvector s(n) with eigenvalue 1, normalized so that its entries sum to 1. We call this the inuence vector, and si(n) is called the inuence of node i. This eigenvector is related to measures of centrality and prestige that have been develop”
Vital nodes identification in complex networks (2016, methodology): “PageRank PageRank algorithm [92] is a famous variant of eigenvector centrality and is used to rank websites in Google search engine and other commercial scenarios [100].”
Spectra and Pseudospectra (2020, methodology): “though it proves very effective for computing Google’s PageRank [18], [22].”
Top 10 algorithms in data mining (2007, background): “The books by Liu [52] and by Langville and Meyer [49] contain in-depth analyses of PageRank and several other link-based algorithms.”
Spectral Graph Theory (2012, background): “For instance, athey are at the heart of Google’s PageRank algorithm for answering web search queries (see [85, 86]).”
Adversarial Web Search (2011, background): “For surveys on many aspects of PageRank, see Langville and Meyer [138, 139].”
An Overview of Social Networks and Economic Applications (2011, background): “It can also be that workers lose skills from being out of work, or that longer spells of unemployment are correlated with unobserved local labor market conditions (see Lynch (1989) for more discussion and references).”