PageRank

👋 About the exercise session

In this lesson, we discuss one of the most signifficant applications of linear algebra - PageRank algorithm.

📓 Notes

Rasmus made a very good note summarizing the main takeaways from the paper about PageRank, so please refer there.

⛳️ Learning goals checklist

After this week, you should be able to:

• translate the given network to matrix \(M\) where \(M = (1 - m)(A + D) + S\)
• compute eigenvector (rank vector) \(x\) corresponding to eigenvalue 1 of the matrix \(M\)
• be able to interpret the rankings that you obtained from the above computation

Congratulations, you have made it trough the Linear algebra part of the course! Next week, we will start with the optimization, see you then!