## Page RankProblem #164 |

Simplified graph of the web with 5 linked pages and their ranks

When we use some web search engine (like Google), we usually do not care how it arranges the results for our queries.

But this is really very interesting thing. Internet contains billions of pages - if I enter words like `Algorithms book`

- it
tells me that about `19`

millions results were found.

But how it cames that the book **Introduction to Algorithms** by MIT is usually at the first page or even at the first place?

Surely Google does not rely on zounds of workers who manually evaluate each page and assign some `importance factor`

to it.

Instead some automatic process is involved, which calculates "worthness" of the pages depeding on how much outer links lead to it. Some obvious rules could be proposed for such calculation:

**General idea:**the page which have more incoming links is more important than another with less amount of links. But surely this may be exploited by cheaters creating many spam pages linking to each other!**Amendment:**the page with few links but from very valuable pages - is also considered important. I.e. if popular mathematical magazine refers to some scientific paper, it is considered to be important paper even if this is the only link leading to it.

For example, at the picture above three `red`

pages only link to each other, so their value is low. Large `green`

page
is referred to by every other page, so it is very important. At last the `yellow`

page has only one incoming link, but
this link is from a **trustworthy** page.

So this is the concept of the Page rank metric. And now we'll try to see how assess it numerically.

Suppose the user starts from some page and navigates further by randomly choosing any of outgoing links. I.e. starting
from the leftmost `red`

node he randomly goes to rightmost `red`

node, then to `green`

, then to `yellow`

and back to
`green`

.

Page rank is just the probability that the user ends at the given page. I.e. if we drop `100`

such **random walkers** into
our network and make them perform enough steps (say `10`

or `30`

), then at the end we can count how many people are
currently at every page and this will give the page rank value in percents!

*Of course it is rather primitive description since, for example, it have no provision against draining all walkers
from nodes or clusters which have no incoming links (like three red nodes in a picture above). Real implementations may
limit number of steps of the walker or introduce some other special limitations.*

Your task is to conduct such an experiment with a given web graph. Initially place several walkers (say `10`

) at each
node (each page) and then for each of them choose some outgoing link and perform transition to other node. Repeat this
several times and output how many people land at each node after that.

**Input data** contains number `N`

of nodes (pages) in a graph - and then number `M`

of edges (hyperlinks between pages).

Next `M`

lines describe one direct edge each by two values - index of the source and target node (indices are in range `0..N-1`

).

**Answer** should contain `N`

integers - `i-th`

value describing how many walkers ended at `i-th`

node.

This experiment is subject to stochastic errors, but your answer would be accepted if it is
close enough to "ideal" - error up to

`2.5%`

is considered OK. You may need to retry few times if your first
submission is not successful.Example:

```
input data:
6 9
0 1
0 3
0 5
1 3
2 0
3 4
4 0
4 2
5 3
answer:
72 23 37 73 70 25
```

In this example `50`

walkers started from each of `6`

pages and continued for about `20`

steps.

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