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Title: Higher-order web link analysis using multilinear algebra.

Linear algebra is a powerful and proven tool in web search. Techniques, such as the PageRank algorithm of Brin and Page and the HITS algorithm of Kleinberg, score web pages based on the principal eigenvector (or singular vector) of a particular non-negative matrix that captures the hyperlink structure of the web graph. We propose and test a new methodology that uses multilinear algebra to elicit more information from a higher-order representation of the hyperlink graph. We start by labeling the edges in our graph with the anchor text of the hyperlinks so that the associated linear algebra representation is a sparse, three-way tensor. The first two dimensions of the tensor represent the web pages while the third dimension adds the anchor text. We then use the rank-1 factors of a multilinear PARAFAC tensor decomposition, which are akin to singular vectors of the SVD, to automatically identify topics in the collection along with the associated authoritative web pages.
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  1. (Sandia National Laboratories, Albuquerque, NM)
Publication Date:
OSTI Identifier:
Report Number(s):
TRN: US201009%%66
DOE Contract Number:
Resource Type:
Technical Report
Research Org:
Sandia National Laboratories
Sponsoring Org:
Country of Publication:
United States
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; ALGEBRA; ALGORITHMS; DIMENSIONS; EIGENVECTORS; VECTORS Linear and Multilinear Algebras, Matrix Theory; Tensor algebra.; Web search engines.; Web sites-Design-Planning.