PageRank, HITS and a unified framework for link analysis
Chris Ding, Xiaofeng He, Parry Husbands, Hongyuan Zha, Horst D. Simon
Abstract
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Chris Ding, Xiaofeng He, Parry Husbands, Hongyuan Zha, Horst D. Simon
Abstract
Open-access reader
Two popular link-based webpage ranking algorithms are (i) PageRank[1] and (ii) HITS (Hypertext Induced Topic Selection)[3]. HITS makes the crucial distinction of hubs and authorities and computes them in a mutually reinforcing way. PageRank considers the hyperlink weight normalization and the equilibrium distribution of random surfers as the citation score. We generalize and combine these key concepts into a unified framework, in which we prove that rankings produced by PageRank and HITS are both highly correlated with the ranking by in-degree and out-degree.
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Two popular link-based webpage ranking algorithms are (i) PageRank[1] and (ii) HITS (Hypertext Induced Topic Selection)[3]. HITS makes the crucial distinction of hubs and authorities and computes them in a mutually reinforcing way. PageRank considers the hyperlink weight normalization and the equilibrium distribution of random surfers as the citation score. We generalize and combine these key concepts into a unified framework, in which we prove that rankings produced by PageRank and HITS are both highly correlated with the ranking by in-degree and out-degree.
Key concepts: PageRank, Hyperlink, Computer science, HITS algorithm, Ranking (information retrieval), Link analysis, Information retrieval, Normalization (sociology)