Exercise

# Changes in PageRank

The PageRank formula \(\vec{PR}=\alpha \cdot A \cdot \vec{PR} + (1-\alpha)\cdot \vec{e}\) can be solved for \(\vec{PR}\) iteratively. In each iteration, the current value of \(\vec{PR}\) is used to compute a new value that is closer to the true value. This means that the difference between the \(\vec{PR}s\) of every two subsequent iterations becomes smaller and smaller until \(\vec{PR}\) converges to the true value and the difference becomes (almost) zero. In this exercise, you will inspect the PageRank algorithm and how it converges.

Instructions

**100 XP**

- Compute one iteration with the PageRank algorithm using
`page.rank()`

with`network`

and indicating`niter=1`

. Extract the vector attribute and assign the result to`iter1`

. - Repeat the last step with
`niter=2`

. Assign the result to`iter2`

. - Compute the sum of the absolute difference between the vectors
`iter1`

and`iter2`

. - We have computed
`iter9`

and`iter10`

in the same way as`iter1`

and`iter2`

. Is the difference between these two iterations less than between iterations 1 and 2.