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All Journal ComEngApp : Computer Engineering and Applications Journal Computer Engineering and Applications Journal (ComEngApp) Computer Engineering and Applications Journal (ComEngApp)
Quanyuan Wu
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Computer Science & IT Engineering

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Using Power-Law Degree Distribution to Accelerate PageRank Zhaoyan Jin; Quanyuan Wu
Computer Engineering and Applications Journal (ComEngApp) December 2012
Publisher : Computer Engineering and Applications Journal (ComEngApp)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1029.103 KB)

Abstract

The PageRank vector of a network is very important, for it can reflect the importance of a Web page in the World Wide Web, or of a people in a social network. However, with the growth of the World Wide Web and social networks, it needs more and more time to compute the PageRank vector of a network. In many real-world applications, the degree and PageRank distributions of these complex networks conform to the Power-Law distribution. This paper utilizes the degree distribution of a network to initialize its PageRank vector, and presents a Power-Law degree distribution accelerating algorithm of PageRank computation. Experiments on four real-world datasets show that the proposed algorithm converges more quickly than the original PageRank algorithm.
Using Power-Law Degree Distribution to Accelerate PageRank Zhaoyan Jin; Quanyuan Wu
Computer Engineering and Applications Journal Vol 1 No 2 (2012)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1029.269 KB) | DOI: 10.18495/comengapp.v1i2.8

Abstract

The PageRank vector of a network is very important, for it can reflect the importance of a Web page in the World Wide Web, or of a people in a social network. However, with the growth of the World Wide Web and social networks, it needs more and more time to compute the PageRank vector of a network. In many real-world applications, the degree and PageRank distributions of these complex networks conform to the Power-Law distribution. This paper utilizes the degree distribution of a network to initialize its PageRank vector, and presents a Power-Law degree distribution accelerating algorithm of PageRank computation. Experiments on four real-world datasets show that the proposed algorithm converges more quickly than the original PageRank algorithm.
Using Power-Law Degree Distribution to Accelerate PageRank Zhaoyan Jin; Quanyuan Wu
Computer Engineering and Applications Journal (ComEngApp) December 2012
Publisher : Computer Engineering and Applications Journal (ComEngApp)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The PageRank vector of a network is very important, for it can reflect the importance of a Web page in the World Wide Web, or of a people in a social network. However, with the growth of the World Wide Web and social networks, it needs more and more time to compute the PageRank vector of a network. In many real-world applications, the degree and PageRank distributions of these complex networks conform to the Power-Law distribution. This paper utilizes the degree distribution of a network to initialize its PageRank vector, and presents a Power-Law degree distribution accelerating algorithm of PageRank computation. Experiments on four real-world datasets show that the proposed algorithm converges more quickly than the original PageRank algorithm.
Using Power-Law Degree Distribution to Accelerate PageRank Jin, Zhaoyan; Wu, Quanyuan
Computer Engineering and Applications Journal (ComEngApp) Vol. 1 No. 2 (2012)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The PageRank vector of a network is very important, for it can reflect the importance of a Web page in the World Wide Web, or of a people in a social network. However, with the growth of the World Wide Web and social networks, it needs more and more time to compute the PageRank vector of a network. In many real-world applications, the degree and PageRank distributions of these complex networks conform to the Power-Law distribution. This paper utilizes the degree distribution of a network to initialize its PageRank vector, and presents a Power-Law degree distribution accelerating algorithm of PageRank computation. Experiments on four real-world datasets show that the proposed algorithm converges more quickly than the original PageRank algorithm.
Co-Authors Zhaoyan Jin Zhaoyan Jin