Google PageRank and Reduced-Order Modelling

Google PageRank is designed to determine the importance of a webpage. To do so, one needs to compute an eigenvector of the Google matrix. We show that this vector can also be found by solving a linear system. Additionally, an adjustment to the PageRank model will be made by considering one of the parameters to be a stochastic variable. We will...

The second eigenvector of the Google matrix and its relation to link spamming

Google PageRank attempts to return the best ranking of websites when searching on the web. To find this ranking, Google introduces a Markov matrix to model the behaviour of internet users. We find that using irreducible closed subsets is an effective way to unfairly increase the PageRank of a website (perform link spamming) and this can be...

Google's PageRank: De tweede eigenwaarde en een variable sprongkans

Google maakt gebruik van PageRank om webpagina's te rangschikken naar hoe belangrijk ze zijn. Het berekenen van de PageRank vereist lineaire algebra. Ook komt Markov theorie hier aan bod. Één van de parameters is een zekere alfa. Deze alfa wordt door velen constant gekozen. Ik heb in dit rapport onderzoek gedaan naar de gevolgen van het variabel...

The PageRank Problem

The thesis is the result of a bachelor research project about Google's PageRank. An analysis of the hyperlink structure of the World Wide Web is made and a model for web surfing studied. Based on this model, some standard methods to compute the PageRank of web pages is investigated. Special attention is given to computing PageRanks by using...