DiscoWeb: Applying Link Analysis to Web Search

Abstract

the deeper eigenvector analysis used in our work and in those projects mentioned above. It turns out that the identification of "high-quality" web pages reduces to a sparse eigenvalue of the adjacency matrix of the linked graph [7, 3]. In 1998, Kleinberg [7] provided the analysis and substantial evidence that each eigenvector creates a web clustering which he called "web communities". The most important web community corresponds to the principal eigenvector and the component values within each eigenvector represent a ranking of web pages. Determining the eigenvectors is computationally intensive since the linked graph can be quite large, e.g. each keyword search could result in millions of page hits. For this reason, the approach taken in most implementations is to determine only the principal eigenvector [7, 3], using the well-known power iterative method [5] for eigenvectors. If the initial approximation is the unit vector then the first iteration in the power method corresponds t

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