$v_2 = A v_1 = \begin{bmatrix} 1/4 \ 1/2 \ 1/4 \end{bmatrix}$
Using the Power Method, we can compute the PageRank scores as: Linear Algebra By Kunquan Lan -fourth Edition- Pearson 2020
The PageRank scores are computed by finding the eigenvector of the matrix $A$ corresponding to the largest eigenvalue, which is equal to 1. This eigenvector represents the stationary distribution of the Markov chain, where each entry represents the probability of being on a particular page. $v_2 = A v_1 = \begin{bmatrix} 1/4 \
The basic idea is to represent the web as a graph, where each web page is a node, and the edges represent hyperlinks between pages. The PageRank algorithm assigns a score to each page, representing its importance or relevance. representing its importance or relevance.