In this guide, we explore what PageRank is, how it works, and take a look at the factors which influence it, as well as why it still matters to SEOs in 2021.
„Viele mieten Links an, um deren Ranking in Suchmaschinen zu verbessern. Lassen Sie sich die Links Ihrer Mitbewerber anzeigen. So erfahren Sie warum diese in der Suchmaschine vor Ihnen stehen oder (hoffentlich) hinter Ihnen.“
The PageRank algorithm is a great way of using collective intelligence to determine the importance of a webpage. There’s a big problem, though, which is that PageRank is difficult to apply to the web as a whole, simply because the web contains so many webpages. While just a few lines of code can be used to implement PageRank on collections of a few thousand webpages, it’s trickier to compute PageRank for larger sets of pages. The underlying problem is that the most direct way to compute the PageRank of n webpages involves inverting an n \times n matrix. Even when n is just a few thousand, this means inverting a matrix containing millions or tens of millions of floating point numbers. This is possible on a typical personal computer, but it’s hard to go much further. In this post, I describe how to compute PageRank for collections containing millions of webpages. My little laptop easily coped with two million pages, using about 650 megabytes of RAM and a few hours of computation
Rankyoo est un site qui vous permet de connaitre la position de votre site internet sur google en se basant sur les mots-clés ou expressions décrivant votre site et son contenu. Mais en vous inscrivant gratuitement, vous pourrez aussi suivre l'évolution de ce positionnement au fil du temps. C'est ce postulat de départ, auquel vient s'ajouter d'autres fonctionnalités et une interface claire et intuitive, qui fait de Rankyoo un site innovant, une mine d'or d'informations qui vous fera gagner énormément de temps.
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S. Brin, and L. Page. Proceedings of the seventh international conference on World Wide Web 7, page 107--117. Amsterdam, The Netherlands, The Netherlands, Elsevier Science Publishers B. V., (1998)
S. Brin, and L. Page. http://ilpubs.stanford.edu:8090/361/, (1998)In this paper, we present Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext. Google is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems. The prototype with a full text and hyperlink database of at least 24 million pages is available at http://google.stanford.edu/. To engineer a search engine is a challenging task. Search engines index tens to hundreds of millions of web pages involving a comparable number of distinct terms. They answer tens of millions of queries every day. Despite the importance of large-scale search engines on the web, very little academic research has been done on them. Furthermore, due to rapid advance in technology and web proliferation, creating a web search engine today is very different from three years ago. This paper provides an in-depth description of our large-scale web search engine -- the first such detailed public description we know of to date. Apart from the problems of scaling traditional search techniques to data of this magnitude, there are new technical challenges involved with using the additional information present in hypertext to produce better search results. This paper addresses this question of how to build a practical large-scale system which can exploit the additional information present in hypertext. Also we look at the problem of how to effectively deal with uncontrolled hypertext collections where anyone can publish anything they want..