Article

Exploiting the hierarchical structure for link analysis

Authors:

Zheng ChenAuthors Info & Claims

SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval

Pages 186 - 193

https://doi.org/10.1145/1076034.1076068

Published: 15 August 2005 Publication History

Abstract

Link analysis algorithms have been extensively used in Web information retrieval. However, current link analysis algorithms generally work on a flat link graph, ignoring the hierarchal structure of the Web graph. They often suffer from two problems: the sparsity of link graph and biased ranking of newly-emerging pages. In this paper, we propose a novel ranking algorithm called Hierarchical Rank as a solution to these two problems, which considers both the hierarchical structure and the link structure of the Web. In this algorithm, Web pages are first aggregated based on their hierarchical structure at directory, host or domain level and link analysis is performed on the aggregated graph. Then, the importance of each node on the aggregated graph is distributed to individual pages belong to the node based on the hierarchical structure. This algorithm allows the importance of linked Web pages to be distributed in the Web page space even when the space is sparse and contains new pages. Experimental results on the .GOV collection of TREC 2003 and 2004 show that hierarchical ranking algorithm consistently outperforms other well-known ranking algorithms, including the PageRank, BlockRank and LayerRank. In addition, experimental results show that link aggregation at the host level is much better than link aggregation at either the domain or directory levels.

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Cited By

Sharma PYadav D(2020)Incremental Refinement of Page Ranking of Web PagesInternational Journal of Information Retrieval Research10.4018/IJIRR.202007010410:3(57-73)Online publication date: 1-Jul-2020
https://doi.org/10.4018/IJIRR.2020070104
Cui YSparkman CLee HLoguinov D(2018)Unsupervised Domain Ranking in Large-Scale Web CrawlsACM Transactions on the Web10.1145/318218012:4(1-29)Online publication date: 27-Sep-2018
https://dl.acm.org/doi/10.1145/3182180
Yang FHu BBai CHan XBen-Othman JGang FLiu JArai M(2017)Product information extraction & analysisProceedings of the 3rd International Conference on Communication and Information Processing10.1145/3162957.3163028(261-267)Online publication date: 24-Nov-2017
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Index Terms

Exploiting the hierarchical structure for link analysis
1. Information systems
  1. Information retrieval

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cover image ACM Conferences

SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval

August 2005

708 pages

ISBN:1595930345

DOI:10.1145/1076034

General Chairs:
Ricardo Baeza-Yates
University of Chile, Chile
,
Nivio Ziviani
Federal University of Minas Gerais, Brazil
,
Program Chairs:
Gary Marchionini
University of North Carolina, USA
,
Alistair Moffat
University of Melbourne, Australia
,
John Tait
University of Sunderland, UK

Copyright © 2005 ACM.

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SIGIR: ACM Special Interest Group on Information Retrieval

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New York, NY, United States

Publication History

Published: 15 August 2005

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SIGIR05: The 28th ACM/SIGIR International Symposium on Information Retrieval 2005

August 15 - 19, 2005

Salvador, Brazil

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

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https://doi.org/10.4018/IJIRR.2020070104
Cui YSparkman CLee HLoguinov D(2018)Unsupervised Domain Ranking in Large-Scale Web CrawlsACM Transactions on the Web10.1145/318218012:4(1-29)Online publication date: 27-Sep-2018
https://dl.acm.org/doi/10.1145/3182180
Yang FHu BBai CHan XBen-Othman JGang FLiu JArai M(2017)Product information extraction & analysisProceedings of the 3rd International Conference on Communication and Information Processing10.1145/3162957.3163028(261-267)Online publication date: 24-Nov-2017
https://dl.acm.org/doi/10.1145/3162957.3163028
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Wang JKang CChang YHan J(2016)Learning hostname preference to enhance search relevanceProceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence10.5555/3061053.3061165(3903-3909)Online publication date: 9-Jul-2016
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Wei WGao BLiu TWang TLi GLi H(2016)A Ranking Approach on Large-Scale Graph With Multidimensional Heterogeneous InformationIEEE Transactions on Cybernetics10.1109/TCYB.2015.241823346:4(930-944)Online publication date: Apr-2016
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Liu WLi GCheng J(2015)Fast PageRank approximation by adaptive samplingKnowledge and Information Systems10.1007/s10115-013-0691-142:1(127-146)Online publication date: 1-Jan-2015
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Fukumoto FSugiyama HSuzuki YMatsuyoshi S(2015)Exploiting Guest Preferences with Aspect-based Sentiment Analysis for Hotel RecommendationKnowledge Discovery, Knowledge Engineering and Knowledge Management10.1007/978-3-319-25840-9_3(34-49)Online publication date: 28-Oct-2015
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