research-article

A Local Method for ObjectRank Estimation

Authors:

Yuto Yamaguchi,

Toshiyuki Amagasa,

Hiroyuki KitagawaAuthors Info & Claims

IIWAS '13: Proceedings of International Conference on Information Integration and Web-based Applications & Services

Pages 92 - 101

https://doi.org/10.1145/2539150.2539177

Published: 02 December 2013 Publication History

Abstract

ObjectRank is a method of link structure analysis to evaluate the importance of objects in a database. ObjectRank is known to be computationally expensive, because it requires iterative computations over a large graph. However, in many real applications, it is sufficient to compute the ObjectRank scores for only small fraction of objects. To address this problem, this paper proposes a novel method for estimating ObjectRank scores for specific objects by applying local computation over partial graphs, thereby allowing us to maintain low computational cost even for large graphs. Our basic idea is that, for a given target node, we induce a local graph by checking the edge weights and pruning the edges with considering their weights. We conduct experiments to compare our method with some comparative methods. The experimental results show that our method can reduce the computational cost while maintaining the accuracy.

References

[1]

S. Abiteboul, M. Preda, and G. Cobena. Adaptive on-line page importance computation. In World Wide Web Conference Series, pages 280--290, 2003.

Digital Library

[2]

A. Balmin, V. Hristidis, and Y. Papakonstantinou. ObjectRank: Authority-Based Keyword Search in Databases. In Very Large Data Bases, pages 564--575, 2004.

Digital Library

[3]

Z. Bar-yossef and L. tal Mashiach. Local approximation of pagerank and reverse pagerank. In International Conference on Information and Knowledge Management, pages 279--288, 2008.

Digital Library

[4]

M. Bressan and L. Pretto. Local computation of PageRank: the ranking side. In International Conference on Information and Knowledge Management, pages 631--640, 2011.

Digital Library

[5]

A. Z. Broder, R. Lempel, F. Maghoul, and J. O. Pedersen. Efficient PageRank approximation via graph aggregation. Information Retrieval, 9:123--138, 2006.

Digital Library

[6]

Y.-Y. Chen, Q. Gan, and T. Suel. Local methods for estimating pagerank values. In International Conference on Information and Knowledge Management, pages 381--389, 2004.

Digital Library

[7]

J. V. Davis and I. S. Dhillon. Estimating the global pagerank of web communities. In Knowledge Discovery and Data Mining, pages 116--125, 2006.

Digital Library

[8]

N. Neubauer, R. Wetzker, and K. Obermayer. Tag spam creates large non-giant connected components. In Adversarial Information Retrieval on the Web, pages 49--52, 2009.

Digital Library

[9]

L. Page, S. Brin, R. Motwani, and T. Winograd. The pagerank citation ranking: Bringing order to the web. Technical Report 1999--66, Stanford InfoLab, November 1999.

[10]

C. Spearman. 'FOOTRULE' FOR MEASURING CORRELATION. British Journal of Psychology, 2:89--108, 1906.

[11]

Y. Sun, J. Han, P. Zhao, Z. Yin, H. Cheng, and T. Wu. RankClus: integrating clustering with ranking for heterogeneous information network analysis. In Extending Database Technology, pages 565--576, 2009.

Digital Library

[12]

J. Tang, L. Yao, D. Zhang, and J. Zhang. A combination approach to web user profiling. ACM TKDD, 5(1):1--44, 2010.

Digital Library

[13]

J. Tang, D. Zhang, and L. Yao. Social network extraction of academic researchers. In ICDM'07, pages 292--301, 2007.

Digital Library

[14]

J. Tang, J. Zhang, R. Jin, Z. Yang, K. Cai, L. Zhang, and Z. Su. Topic level expertise search over heterogeneous networks. Machine Learning Journal, 82(2):211--237, 2011.

Digital Library

[15]

J. Tang, J. Zhang, L. Yao, J. Li, L. Zhang, and Z. Su. Arnetminer: Extraction and mining of academic social networks. In KDD'08, pages 990--998, 2008.

Digital Library

[16]

A. Vattani, D. Chakrabarti, and M. Gurevich. Preserving personalized pagerank in subgraphs. In International Conference on Machine Learning, pages 793--800, 2011.

[17]

Y. Wu and L. Raschid. ApproxRank: Estimating Rank for a Subgraph,. In International Conference on Data Engineering, pages 54--65, 2009.

Digital Library

[18]

G.-R. Xue, H.-J. Zeng, Z. Chen, W.-Y. Ma, H.-J. Zhang, and C.-J. Lu. Implicit link analysis for small web search. In Research and Development in Information Retrieval, pages 56--63, 2003.

Digital Library

[19]

Y. Yamaguchi, T. Takahashi, T. Amagasa, and H. Kitagawa. TURank: Twitter User Ranking Based on User-Tweet Graph Analysis. In Web Information Systems Engineering, pages 240--253, 2010.

Digital Library

Cited By

Shiokawa H(2021)Fast ObjectRank for Large Knowledge DatabasesThe Semantic Web – ISWC 202110.1007/978-3-030-88361-4_13(217-234)Online publication date: 30-Sep-2021
https://doi.org/10.1007/978-3-030-88361-4_13
Sakakura YYamaguchi YAmagasa TKitagawa H(2014)An Improved Method for Efficient PageRank EstimationDatabase and Expert Systems Applications10.1007/978-3-319-10085-2_19(208-222)Online publication date: 2014
https://doi.org/10.1007/978-3-319-10085-2_19

Index Terms

A Local Method for ObjectRank Estimation
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Other conferences

IIWAS '13: Proceedings of International Conference on Information Integration and Web-based Applications & Services

December 2013

753 pages

ISBN:9781450321136

DOI:10.1145/2539150

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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@WAS: International Organization of Information Integration and Web-based Applications and Services

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 December 2013

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IIWAS '13

IIWAS '13: The 15th International Conference on Information Integration and Web-based Applications & Services

December 2 - 4, 2013

Vienna, Austria

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Cited By

Shiokawa H(2021)Fast ObjectRank for Large Knowledge DatabasesThe Semantic Web – ISWC 202110.1007/978-3-030-88361-4_13(217-234)Online publication date: 30-Sep-2021
https://doi.org/10.1007/978-3-030-88361-4_13
Sakakura YYamaguchi YAmagasa TKitagawa H(2014)An Improved Method for Efficient PageRank EstimationDatabase and Expert Systems Applications10.1007/978-3-319-10085-2_19(208-222)Online publication date: 2014
https://doi.org/10.1007/978-3-319-10085-2_19

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