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1.2 ⋅ pagerank on a graph 3 Figure 1.1 – A pictorial illustration of the PageRank model. blob is also known as teleporting or resetting because it can move the surfer anywhere in the web. 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. 1.2 pagerank on a graph Although PageRank models a random surfer on the web and computes the probability of finding this surfer at any given page, the output of PageRank is a number for each page on the web related to its importance. In the previous section, we explained PageRank where a surfer sits at a current page in the web. The proper mathematical abstraction of the linked nature of the web is aPDF Image | Instagram Cheat Sheet
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