Collective Evolutionary Indexing of Multimedia Objects
ICCSA '09 Proceedings of the International Conference on Computational Science and Its Applications: Part I
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Many people use search engines every day to retrieve documents from the Web. Although the social influence of search engine rankings has become significant, ranking algorithms are not disclosed. In this paper, we have investigated three major search engine rankings by analyzing two kinds of data. One is the weekly ranking snapshots of top 250 Web pages we collected for almost one year by submitting 1,000 pre-selected queries; the other comprises back-linked Web pages gathered by our own Web crawling. As a result, we have confirmed that (1) several top 10 rankings are mutually similar, however, the following ranked Web pages are almost different, (2) ranking transitions have their own characteristics, and (3) each search engine's ranking has its own correlation with the number of back-linked Web pages.