Linear Algebra By Kunquan Lan -fourth Edition- Pearson 2020 < EASY — 2024 >

The Google PageRank algorithm is a great example of how Linear Algebra is used in real-world applications. By representing the web as a graph and using Linear Algebra techniques, such as eigenvalues and eigenvectors, we can compute the importance of each web page and rank them accordingly.

Imagine you're searching for information on the internet, and you want to find the most relevant web pages related to a specific topic. Google's PageRank algorithm uses Linear Algebra to solve this problem. Linear Algebra By Kunquan Lan -fourth Edition- Pearson 2020

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. The Google PageRank algorithm is a great example

The PageRank scores indicate that Page 2 is the most important page, followed by Pages 1 and 3. Google's PageRank algorithm uses Linear Algebra to solve

Page 1 links to Page 2 and Page 3 Page 2 links to Page 1 and Page 3 Page 3 links to Page 2

Let's say we have a set of $n$ web pages, and we want to compute the PageRank scores. We can create a matrix $A$ of size $n \times n$, where the entry $a_{ij}$ represents the probability of transitioning from page $j$ to page $i$. If page $j$ has a hyperlink to page $i$, then $a_{ij} = \frac{1}{d_j}$, where $d_j$ is the number of hyperlinks on page $j$. If page $j$ does not have a hyperlink to page $i$, then $a_{ij} = 0$.

$v_2 = A v_1 = \begin{bmatrix} 1/4 \ 1/2 \ 1/4 \end{bmatrix}$