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Now imagine that we let the surfer run for a long time. The PageRank of a page is the probability of finding the surfer at that page as the surfing time becomes infinite. A key assumption behind PageRank is that pages where we are more likely to find the random surfer are more important pages and thus we can view the PageRank as a measure of the page’s importance. In reality, the PageRank problem is expressed as a mathematical equation that generates a number between 0 and 1 for each page. We’ll delve into the mathematics of PageRank in chapter 2. The focus of my thesis is investigating what happens when varying the α parameter. For a preview, let’s look only at the pages in Wikipedia [Various, 2009b]. In this case, the surfer ignores all the links to the actual source material outside of the Wikipedia system. Table 1.1 shows the titles of the 10 pages with highest PageRank in Wikipedia. A few things change with the α parameter. When α is 0.5, the pages are focused on countries, whereas when α is 0.99, the pages are focused on the encyclopedia infrastructure. For example, the page “Category:Wikipedia administration” describes the administration of the encyclopedia itself. 1.1 ⋅ pagerank and web search 3 α = 0.50 United States C:Living people France Germany England United Kingdom Canada Japan Poland Australia α = 0.85 United States C:Main topic classif. C:Contents C:Living people C:Ctgs. by country United Kingdom C:Fundamental C:Ctgs. by topic C:Wikipedia admin. France α = 0.99 C:Contents C:Main topic classif. C:Fundamental United States C:Wikipedia admin. P:List of portals P:Contents/Portals C:Portals C:Society C:Ctgs. by topic Table 1.1 – Highest PageRank pages in Wikipedia. The set of pages in Wikipedia with the highest PageRank scores for three values of the parameter α. The prefix “C:” denotes a category page and any term with a period is abbreviated.PDF Image | ALGORITHMS FOR PAGERANK SENSITIVITY DISSERTATION
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