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Spectral ranking

Bibliographic Data

ID6161634
AuthorsSebastiano Vigna (0000-0002-3257-651X, University of Milan, corresponding author)
Year2016
Volume4
Issue4
Pages433-445
Publication date2016-11-21
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueNetwork Science (JOURNAL)
Journal identifiersISSN: 2050-1250 • E-ISSN: 2050-1242
PublisherCambridge University Press (PUBLISHER • US)
DOI10.1017/nws.2016.21
OpenAlexW3037400782
LanguageEN
Citations received5
References cited30

We sketch the history of spectral ranking —a general umbrella name for techniques that apply the theory of linear maps (in particular, eigenvalues and eigenvectors) to matrices that do not represent geometric transformations, but rather some kind of relationship between entities . Albeit recently made famous by the ample press coverage of Google's PageRank algorithm, spectral ranking was devised more than 60 years ago, almost exactly in the same terms, and has been studied in psychology, social sciences, bibliometrics, economy, and choice theory. We describe the contribution given by previous scholars in precise and modern mathematical terms: Along the way, we show how to express in a general way damped rankings, such as Katz's index, as dominant eigenvectors of perturbed matrices, and then use results on the Drazin inverse to go back to the dominant eigenvectors by a limit process. The result suggests a regularized definition of spectral ranking that yields for a general matrix a unique vector depending on a boundary condition

Algebra over a field · Algorithm · Combinatorics · Eigenvalues and eigenvectors · Information retrieval · Matrix (chemical analysis · PageRank · Physics · Pure mathematics · Rank (graph theory · Ranking (information retrieval · Sketch · Complex Network Analysis Techniques · Computer Science · Mathematics · Topological and Geometric Data Analysis

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Unique citing works5
Citations per year0.56
Citation span2017 - 2024 (8)
Citation velocityrecent
Highly citedNo
Citation typesNeutral: 5

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