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DRank+: A directory based PageRank prediction method for fast pagerank convergence
Conference paper

DRank+: A directory based PageRank prediction method for fast pagerank convergence

Hung-Yu Kao, Chia-Sheng Liu, Yu-Chuan Tsai, Chia-Chun Shih and Tse-Ming Tsai
WEBIST 2008 - 4th International Conference on Web Information Systems and Technologies, Proceedings, Vol.2, pp.175-180
2008

Abstract

Hierarchical structure Link analysis Page quality PageRank Search engine Web graph Artificial Intelligence Computer Networks and Communications Computer Science Applications Information Systems and Management
In recent years, most part of search engines use link analysis algorithms to measure the importance of web pages. The most famous link analysis algorithm is PageRank algorithm. However, previous researches in recent years have found that there exists an inherent bias against newly created pages in PageRank. In the previous work, a new ranking algorithm called DRank has been proposed to solve this issue. It utilizes the cluster phenomenon of PageRank in a directory to predict the possible importance of pages in the future and to diminish the inherent bias of search engines to new pages. In this paper, we modify the original DRank algorithm to complement the weaker part of DRank which could fail while the number of pages in directory is not enough. In our experiments, the augmented algorithm, i.e., DRank+ algorithm, obtains more accuracy in predicting the importance score of pages at next time stage than the original DRank algorithm. DRank+ not only alleviates the bias of newly created pages successfully but also reaches more accuracy than Page Quality and original DRank in predicting the importance of newly created pages.

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