There are two different ways that we can add to this graph.

Let's take a look at that. Let's think about adding some edges here.

In our last question, we used a list of tuples to represent a graph where each tuple represented and edge between nodes.

So from A, we look at the neighbors of A. The neighbors of A are B and C.

Its count up the number of books in common between character one and character two, which is really just what we wanted, but it's done in terms of a triple nested loop here.

So this altogether is going to get run once for each node so this part of the algorithm actually takes time and logn.

Edge Smoothing

We use the Greek letter Epsilon.

What happens if all the nodes that are not in the beginning and ending nodes have to had even degree because the path has to go in one edge and out another edge.

So Kv which is the number of neighbors is 4.

And the semantics are the i jth entry of the matrix is 1.

Remember the H value and the L value were defined specifically to be well, you follow a bunch of green edges and then you follow one red edge.

What kind of graphs are K colorable? Graphs with no edges.

We have an edge between node 1 and 2, an edge between node 2 and 4, and edge between node 5 and 9, etc.

This is just kind of a random selection of big cities in the US, but with emphasis on places I've lived.

This tells you where you're going, this where you are and this where you were.

Find Edges

Detect Edges

Edge Detection

It has to do this which using the implementation that we're looking at is another end.

For example, if nodes was 1, 2, 3, one possible solution is a graph that has an edge between 1 and 3, and edge between 1 and 2, and an edge between 2 and 3.

That's where we started.

Top Edge Detection

Right Edge Detection

Bottom Edge Detection

Left Edge Detection

Because all the lessons we've learned is that a time or develop a time.

Right?

So given all these nicely decorated nodes, we now have a rule for figuring out which ones are the bridge edges and this is the rule.

I'll just hold on to it for a while.

The approximate diameter, it's a three inch (3 ) shell mill

It's time! It's time. It's time.

50 minutes is just 50 60 hours or 5 6 hours of time is equal to 5 over 6 hours.

A blood vessel exploded in the left half of my brain.

If we add an edge without a corresponding node, the number of regions goes up by 1.

This is called, again, the convolution of the image.

It's time for school, time for school.

And when I started in with the eight hours and the 10 hours and the 12 hours and the 14 hours and the 15 hours and the 24 hour swims,

Eight, nine hours.

This time you have eight.

But with a Warren Ellis edge.

You have a Warren Ellis edge.

This is still not quite what we're looking for, but let's do an analysis of this graph then call it a day.

So the key question here is the relationship between characteristic cooling time.

For each node, we find the shortest distance and then for each node and each of its neighbors, that is to say for each edge, we possibly reduce it's distance of a node.