A fast PageRank convergence method based on the cluster prediction

Hung-Yu Kao, Seng Feng Lin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

In recent years, search engines have already played the key roles among Web applications, and link analysis algorithms are the major methods to measure the important values of Web pages. These algorithms employ the conventional flat Web graph built by Web pages and link relations of Web pages to obtain the relative importance of Web objects. Previous researches have observed that PageRank-like link analysis algorithms have a bias against newly created Web pages. A new ranking algorithm called Page Quality was then proposed to solve this issue. Page Quality predicates future ranking values by the difference rate between the current ranking value and the previous ranking value. In this paper, we propose a new algorithm called DRank to diminish the bias of PageRank-like link analysis algorithms, and attain the better performance than Page Quality. In this algorithm, we model Web graph as a three-layer graph which includes Host Graph, Directory Graph and Page Graph by using the hierarchical structure of URLs and the structure of link relation of Web pages. We calculate the importance of Hosts, Directories and Pages by weighted graph we built and then the clustering distribution of PageRank values of pages within directories is observed. We can then predicate the more accurate values of page importance to diminish the bias of newly created pages by the clustering characteristic of PageRank. Experiment results show that DRank algorithm works well on predicating future ranking values of pages and outperform Page Quality.

Original languageEnglish
Title of host publicationProceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, WI 2007
Pages593-599
Number of pages7
DOIs
Publication statusPublished - 2007 Dec 1
EventIEEE/WIC/ACM International Conference on Web Intelligence, WI 2007 - Silicon Valley, CA, United States
Duration: 2007 Nov 22007 Nov 5

Publication series

NameProceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, WI 2007

Other

OtherIEEE/WIC/ACM International Conference on Web Intelligence, WI 2007
CountryUnited States
CitySilicon Valley, CA
Period07-11-0207-11-05

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All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications

Cite this

Kao, H-Y., & Lin, S. F. (2007). A fast PageRank convergence method based on the cluster prediction. In Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, WI 2007 (pp. 593-599). [4427158] (Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, WI 2007). https://doi.org/10.1109/WI.2007.7