PageRankalgoritmissa

PageRankalgoritmissa is a method for ranking web pages that was originally developed by Larry Page and Sergey Brin at Stanford University. It is used by Google Search to rank web pages in their search engine results. The algorithm works by counting the number of links and the PageRank of the links pointing to a particular page. The assumption is that more important websites are likely to receive more links from other websites.

The core idea behind PageRank is to simulate a random surfer. Imagine a person randomly clicking on links on the web. PageRank estimates the probability that this random surfer will end up on any given page. Pages that are linked to by many pages, especially by pages that are themselves highly ranked, are considered more important and thus receive a higher PageRank score.

The algorithm is iterative. It starts with an initial PageRank for all pages and then repeatedly updates these scores based on the links between pages. The process continues until the PageRank scores stabilize. A page's PageRank is not solely determined by the number of incoming links, but also by the PageRank of the pages that link to it. A link from a high-PageRank page contributes more to the linked page's score than a link from a low-PageRank page. This recursive nature is what gives PageRank its power.