# MPageRank

Problems which have a huge database always issue challenges to scientists. Evaluating importance of Webs is an interesting example. This problem should be very difficult about not only computation, but also storage since the Web environment contains around 10 billions Web pages. Basing on the “random surfer” idea of PageRank algorithm, MPageRank greatly improves results of Web search by applying a probabilistic model on the link structure of Webs to evaluate “authority” of Webs. Unlike PageRank, in MPageRank, a Web now has diﬀerent ranking scores which depend on the given multi topics. By assigning a value characterizing a relationship between content of pages and a popular topic, we would like to introduce some new notions such as the inﬂuence of page and the stability of rank score vector to evaluate the stability of Web environment. However, the main idea of establishing the MPageRank model is to partition our Web gra! ph into smaller-size Web subgraphs. As a consequence of evaluation and rejection about pages inﬂuence weakly to other pages, the rank score of pages of the original Web graph can be approximated from the rank score of pages in the new partition Web graph.

## References in zbMATH (referenced in 1 article , 1 standard article )

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