In previous research it has been shown that link-based web page metrics can be used to predict experts' assessment of quality. We are interested in a related question: do expert rankings of real-world entities correlate with search engine rankings of corresponding web resources? For example, each year US News & World Report publishes a list of (among others) top 50 graduate business schools. Does their expert ranking correlate with the search engine ranking of the URLs of those business schools? To answer this question we conducted 9 experiments using 8 expert rankings on a range of academic, athletic, financial and popular culture topics. We compared the expert rankings with the rankings in Google, Live Search (formerly MSN) and Yahoo (with list lengths of 10, 25, and 50). In 57 search engine vs. expert comparisons, only 1 strong and 4 moderate correlations were statistically significant. In 42 inter-search engine comparisons, only 2 strong and 4 moderate correlations were statistically significant. The correlations appeared to decrease with the size of the lists: the 3 strong correlations were for lists of 10, the 8 moderate correlations were for lists of 25, and no correlations were found for lists of 50.
Upstill, Craswell and Hawking @cite_8 studied the PageRank and indegree of URLs for Fortune 500 and Fortune Most Admired companies. They found companies on those lists averaged 1 point more PageRank (via the Google toolbar's self-reported 0-10 scale) than companies on the list. They also found that IT companies typically had higher PageRank than non-IT companies. Similar to @cite_7 , they found indegree highly correlated with PageRank.
@cite_7 @cite_8
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Measures based on the Link Recommendation Assumption are hypothesised to help modern Web search engines rank ‘important, high quality’ pages ahead of relevant but less valuable pages and to reject ‘spam’. We tested these hypotheses using inlink counts and PageRank scores readily obtainable from search engines Google and Fast. We found that the average Google-reported PageRank of websites operated by Fortune 500 companies was approximately one point higher than the average for a large selection of companies. The same was true for Fortune Most Admired companies. A substantially bigger difference was observed in favour of companies with famous brands. Investigating less desirable biases, we found a one point bias toward technology companies, and a two point bias in favour of IT companies listed in the Wired 40. We found negligible bias in favour of US companies. Log of indegree was highly correlated with Google-reported PageRank scores, and just as effective when predicting desirable company attributes. Further, we found that PageRank scores for sites within a known spam network were no lower than would be expected on the basis of their indegree. We encounter no compelling evidence to support the use of PageRank over indegree. Since the publication of Brin and Page's paper on PageRank, many in the Web community have depended on PageRank for the static (query-independent) ordering of Web pages. We show that we can significantly outperform PageRank using features that are independent of the link structure of the Web. We gain a further boost in accuracy by using data on the frequency at which users visit Web pages. We use RankNet, a ranking machine learning algorithm, to combine these and other static features based on anchor text and domain characteristics. The resulting model achieves a static ranking pairwise accuracy of 67.3 (vs. 56.7 for PageRank or 50 for random). Ranking has been applied in many domains using recommendation systems such as search engine, e-commerce, and so on. We will introduce and study N-linear mutual ranking, which can rank n classes of objects at once. The ranking scores of these classes are dependent to the others. For instance, PageRank by Google is a 2-linear mutual ranking, which ranks the webpages and links at once. Particularly, we focus to N-star ranking model and demonstrate it in ranking conference and journal problems. We have conducted the experiments for the models in which the citations are not considered. The experimental results are based on the DBLP dataset, which contains more than one million papers, authors and thousands of conferences and journals in computer science. Finally, N-star ranking is a very strong ranking algorithm can be applied in many real-world problems. We show that the empirical distribution of the PageRank values in a large set of Web pages does not follow a power-law except for some particular choices of the damping factor. We argue that for a graph with an in-degree distribution following a power-law with exponent between 2.1 and 2.2, choosing a damping factor around 0.85 for PageRank yields a power-law distribution of its values. We suggest that power-law distributions of PageRank in Web graphs have been observed because the typical damping factor used in practice is between 0.85 and 0.90.