So, there's a lot of interesting stories that we wouldn't have time to go into.
Let's quickly summarize we have seen so far in this lecture.
The key concept is that we can view
the collection of hyperlinked webpages as a network.
There's a graph that we represent by matrix such as H, H hat and G.
There is also a functionality of navigation that we're implicitly
modeling through the way we construct these matrices.
You can disagree with those models.
But so far Google search result has proven to be quite useful.
And this is a general theme that connectivity
pattern provides a hint on the node importance.
Come back to this in lecture four with
three more additional importance matrix suitable for other purposes.
So
Pagerank is famous algorithm which is saw and construct uniquely
defines and then efficiently computes a consistent set of importance
scores based on these ideas, and one of the angles
we just saw and emphasized is the dominant Eigen vector angle.
That's the pi star transpose equals pi star transpose times Google matrix G.