The powerRank web link analysis algorithm

Yizhou Lu; Benyu Zhang; Wensi Xi; Zheng Chen; Yi Liu; Michael R. Lyu; Wei-Ying Ma

doi:10.1145/1013367.1013422

The powerRank web link analysis algorithm

Yizhou Lu
, Benyu Zhang
, Wensi Xi
, Zheng Chen
, Yi Liu
, Michael R. Lyu
, Wei-Ying Ma

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

4 Citations (Scopus)

Abstract

The web graph follows the power law distribution and has a hierarchy structure. But neither the PageRank algorithm nor any of its improvements leverage these attributes. In this paper, we propose a novel link analysis algorithm "the PowerRank algorithm", which makes use of the power law distribution attribute and the hierarchy structure of the web graph. The algorithm consists two parts. In the first part, special treatment is applied to the web pages with low "importance" score. In the second part, the global "importance" score for each web page is obtained by combining those scores together. Our experimental results show that: 1) The PowerRank algorithm computes 10%-30% faster than PageRank algorithm. 2) Top web pages in PowerRank algorithm remain similar to that of the PageRank algorithm.
Copyright is held by the author/owner(s).

Original language	English
Title of host publication	Thirteenth International World Wide Web Conference Proceedings, WWW2004
Pages	986-987
DOIs	https://doi.org/10.1145/1013367.1013422
Publication status	Published - 2004
Externally published	Yes
Event	Thirteenth International World Wide Web Conference Proceedings, WWW2004 - New York, NY, United States Duration: 17 May 2004 → 22 May 2004

Publication series

Name	Thirteenth International World Wide Web Conference Proceedings, WWW2004

Conference

Conference	Thirteenth International World Wide Web Conference Proceedings, WWW2004
Place	United States
City	New York, NY
Period	17/05/04 → 22/05/04

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Funding

The work was done in Microsoft Research Asia; and the authors from CUHK were partially supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. CUHK4182/03E)

Research Keywords

Hierarchy structure
Page rank algorithm
Power distribution

RGC Funding Information

RGC-funded

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10.1145/1013367.1013422

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Cite this

@inproceedings{f427a86049274512ad1adafb834411b1,

title = "The powerRank web link analysis algorithm",

abstract = "The web graph follows the power law distribution and has a hierarchy structure. But neither the PageRank algorithm nor any of its improvements leverage these attributes. In this paper, we propose a novel link analysis algorithm {"}the PowerRank algorithm{"}, which makes use of the power law distribution attribute and the hierarchy structure of the web graph. The algorithm consists two parts. In the first part, special treatment is applied to the web pages with low {"}importance{"} score. In the second part, the global {"}importance{"} score for each web page is obtained by combining those scores together. Our experimental results show that: 1) The PowerRank algorithm computes 10\%-30\% faster than PageRank algorithm. 2) Top web pages in PowerRank algorithm remain similar to that of the PageRank algorithm. Copyright is held by the author/owner(s).",

keywords = "Hierarchy structure, Page rank algorithm, Power distribution",

author = "Yizhou Lu and Benyu Zhang and Wensi Xi and Zheng Chen and Yi Liu and Lyu, \{Michael R.\} and Wei-Ying Ma",

note = "Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].; Thirteenth International World Wide Web Conference Proceedings, WWW2004 ; Conference date: 17-05-2004 Through 22-05-2004",

year = "2004",

doi = "10.1145/1013367.1013422",

language = "English",

isbn = "158113844",

series = "Thirteenth International World Wide Web Conference Proceedings, WWW2004",

pages = "986--987",

booktitle = "Thirteenth International World Wide Web Conference Proceedings, WWW2004",

}

Lu, Y, Zhang, B, Xi, W, Chen, Z, Liu, Y, Lyu, MR & Ma, W-Y 2004, The powerRank web link analysis algorithm. in Thirteenth International World Wide Web Conference Proceedings, WWW2004. Thirteenth International World Wide Web Conference Proceedings, WWW2004, pp. 986-987, Thirteenth International World Wide Web Conference Proceedings, WWW2004, New York, NY, United States, 17/05/04. https://doi.org/10.1145/1013367.1013422

The powerRank web link analysis algorithm. / Lu, Yizhou; Zhang, Benyu; Xi, Wensi et al.
Thirteenth International World Wide Web Conference Proceedings, WWW2004. 2004. p. 986-987 (Thirteenth International World Wide Web Conference Proceedings, WWW2004).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - The powerRank web link analysis algorithm

AU - Lu, Yizhou

AU - Zhang, Benyu

AU - Xi, Wensi

AU - Chen, Zheng

AU - Liu, Yi

AU - Lyu, Michael R.

AU - Ma, Wei-Ying

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

PY - 2004

Y1 - 2004

N2 - The web graph follows the power law distribution and has a hierarchy structure. But neither the PageRank algorithm nor any of its improvements leverage these attributes. In this paper, we propose a novel link analysis algorithm "the PowerRank algorithm", which makes use of the power law distribution attribute and the hierarchy structure of the web graph. The algorithm consists two parts. In the first part, special treatment is applied to the web pages with low "importance" score. In the second part, the global "importance" score for each web page is obtained by combining those scores together. Our experimental results show that: 1) The PowerRank algorithm computes 10%-30% faster than PageRank algorithm. 2) Top web pages in PowerRank algorithm remain similar to that of the PageRank algorithm. Copyright is held by the author/owner(s).

AB - The web graph follows the power law distribution and has a hierarchy structure. But neither the PageRank algorithm nor any of its improvements leverage these attributes. In this paper, we propose a novel link analysis algorithm "the PowerRank algorithm", which makes use of the power law distribution attribute and the hierarchy structure of the web graph. The algorithm consists two parts. In the first part, special treatment is applied to the web pages with low "importance" score. In the second part, the global "importance" score for each web page is obtained by combining those scores together. Our experimental results show that: 1) The PowerRank algorithm computes 10%-30% faster than PageRank algorithm. 2) Top web pages in PowerRank algorithm remain similar to that of the PageRank algorithm. Copyright is held by the author/owner(s).

KW - Hierarchy structure

KW - Page rank algorithm

KW - Power distribution

UR - https://www.scopus.com/pages/publications/19944371750

UR - https://www.scopus.com/pages/publications/19944371750#tab=impact

U2 - 10.1145/1013367.1013422

DO - 10.1145/1013367.1013422

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 158113844

SN - 9781581138443

T3 - Thirteenth International World Wide Web Conference Proceedings, WWW2004

SP - 986

EP - 987

BT - Thirteenth International World Wide Web Conference Proceedings, WWW2004

T2 - Thirteenth International World Wide Web Conference Proceedings, WWW2004

Y2 - 17 May 2004 through 22 May 2004

ER -

The powerRank web link analysis algorithm

Abstract

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Bibliographical note

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Research Keywords

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