If you have ever used a navigation service to find optimal route and estimate time to destination, you've used algorithms on graphs. Graphs arise in various real-world situations as there are road networks, computer networks and, most recently, social networks! If you're looking for the fastest time to get to work, cheapest way to connect set of computers into a network or efficient algorithm to automatically find communities and opinion leaders in Facebook, you're going to work with graphs and algorithms on graphs. In this course, you will first learn what a graph is and what are some of the most important properties. Then you'll learn several ways to traverse graphs and how you can do useful things while traversing the graph in some order. We will then talk about shortest paths algorithms — from the basic ones to those which open door for 1000000 times faster algorithms used in Google Maps and other navigational services. You will use these algorithms if you choose to work on our Fast Shortest Routes industrial capstone project. We will finish with minimum spanning trees which are used to plan road, telephone and computer networks and also find applications in clustering and approximate algorithms.
Directed Acyclic Graphs
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Из курса от партнера University of California San Diego
Algorithms on Graphs
953 оценки
University of California San Diego
953 оценки
Курс 3 из 6 — Specialization Data Structures and Algorithms
Из урока
Decomposition of Graphs 2
This week we continue to study graph decomposition algorithms, but now for directed graphs.
Познакомьтесь с преподавателями
Alexander S. KulikovVisiting Professor
Department of Computer Science and Engineering
Michael LevinLecturer
Computer Science
Daniel M KaneAssistant Professor
Department of Computer Science and Engineering / Department of Mathematics
Neil RhodesAdjunct Faculty
Computer Science and Engineering
Hello, everybody.
Welcome back to the graph algorithms course.
Today, we're going to start talking about directed graphs versus undirected,
in particular, talk about directed acyclic graphs and some of their properties.
So what's the motivation for this?
The point is that sometimes we want to talk about the edges of
a graph that have a direction.
This is just because I mean, sometimes, like pairs of things are related in a way,
but the relation isn't symmetric.
One of them comes before the other, or it's a one way viewership or
something like that.
And so we define a directed graph to be a graph, where each edge has sort of
a designated start end and an end end.
So what are examples where you might want to use this?
For example, if you've got a city map where lots of your streets are one way
roads, the direction that the road's pointing is actually very relevant if you
want to navigate the city.
You can't just follow any series of roads you like because you'll
be driving down some one way streets in the wrong order, so you really
do need to keep track of this orientation if you want to navigate the city.
But then also some of the examples that we gave, links between webpages,
the web graph is probably a directed graph, because usually,
if you've got two webpages and A links to B, B probably doesn't link back to A.
Similarly, if you have followers on a social network,
it depends on the social network.
I mean, in Facebook, friendships are symmetric.
So they're sort of two-directional.
That might be an undirected graph.
But on lots of them, you can follow somebody without them following you, and
so then you end up with wanting to have a directed relation for
when someone's following someone else.
A final example that we'll look at in some more detail today are sort of dependencies
between tasks.
So, we have this directed graph, and we've already built up a lot of this theory that
works for undirected graphs with this sort of exploring DFS algorithms.
But most of this sort of actually still holds for directed graphs.
We can still run DFS on a directed graph.
The slight modification now is that when we run our explores,
we only want to check for directed edges out of v.
So when we say for all neighbors w of v, if w has not been visited, etc,
we really want to say neighbors where v points to w, not the other way around.
And what this will do is it means that when we explore v,
we find things that are actually reachable from v,
using the edges in the direction that they're intended.
So we're only sort of allowed to follow these one-way roads
in their appropriate direction.
Now using this new depth first search, we can still compute pre- and post-orderings.
They still have many of the same properties.
The algorithm for DFS still runs in linear time.
Basically, everything's the same.
The context is now a little bit more general.
Okay, so let's look at a sort of specific example,
where directed graphs are important.
In particular, suppose that we have the following morning routine.
We've gotta do a bunch of things every morning.
We need to wake up, we need to get dressed, we need to eat breakfast,
we need to go to work, we need to shower, all of these things.
We need to do these things in some order, but we can't do them in any old order.
Because well, we need to wake up before we get showered.
And we need to dress before we go to work.
And we need to eat breakfast before we go to work, and all of this stuff.
And one way of representing these sorts of dependencies is by a directed graph.
If we need to do A before we can do B,
then we draw a directed edge from A pointing to B.
And so, this gives us some sort of dependency relation.
And it's not just these sort of trivial examples of how do I
get dressed in the morning.
But if you've got some sort of complicated system of libraries, where some of them
require other ones in order to work, you can end up with some sort of similar
graphic dependencies, which you actually do need similar techniques to handle.
Okay, so what do we do when we have these dependencies?
Well, one of the things that we'd like to do is we'd like to find
the ordering of the tasks in a way that respects these dependencies.
We'd like to wake up before we, well, fine.
For example, suppose that we woke up at 7 o'clock and then showered at 7:05,
got dressed at 7:15, had breakfast at 7:20, and went to work at 7:30.
This puts all of our events in some order.
And you'll note that this order respects all of our dependencies.
We wake up before we shower, before we get dressed, before we go to work.
And we eat breakfast before we go to work, and everything works out nice.
And so, if we have one of these dependency graphs,
we'd like to linearly order the vertices to respect these dependencies.
Now one thing to ask is, is it always possible to do this?
And it turns out the answer is no.
The sort of best counter example is this following chicken and egg problem.
I mean, the point is that you need a chicken in order to lay eggs, and
you need eggs to hatch chickens.
And so you can't like put them in some order where one of them comes first.
If you put chickens first, then it would just point you from
eggs back to chickens going in the wrong direction.
You put eggs first,
there's this pointer from chickens back to eggs in the wrong direction.
Without someplace to have gotten started,
there's sort of no way you can get this ordering and go.
In fact, in general there's a slightly more complicated way in which this can
fail to happen.
In fact, if your graph has any cycle, a cycle here is a sequence of vertices, v1,
v2 to vn, such that each one connects to the next using a directed edge.
So you've got a bunch of vertices arranged.
The circles just that each one connects to the next one all the way around.
And the theorem is that if G contains a cycle, it cannot be linearly ordered.
Okay, so just to make, well, fine.
Let's take a look at the proof here.
So suppose their graph has a cycle,
v1 through vn, everything connected up in order.
And suppose that additionally, we can linearly order this graph.
Well, if you linearly order these things, there are finitely many.
One of them needs to come first.
So suppose that vk comes first in this order.
But now we're putting vk before vk-1, and vk-1 points to it, so
we have an arrow pointing in the wrong direction, which gives us a contradiction.
So, if we have a cycle, we cannot be linearly ordered.
So, what this means is that in order to be linearly orderable,
you need to be what's known as a directed acyclic graph or DAG.
And this is just a name for a directed graph that has no cycles, fair enough.
Now, by the above theorem, it is necessary to be a DAG in order to linearly order.
But one question we should ask ourselves perhaps is, is it sufficient?
Can we necessarily linearly order it if it's a DAG?
Well, okay, this is a question to ask a little bit later, but for now,
let's just review.
We have the following four graphs.
Note that the edges here are sort of the same, except for their orientations.
And which one of these graphs is a DAG?
Well, it turns out that only A is.
B has the cycle noted in red, and C has this other cycle noted in red.
But if you work out A,
you can actually see that it does not have any cycles to it.
Okay, but the question that we were posing was,
is it the case that any DAG can be linearly ordered?
And the theorem that we'll actually show is that yes,
if you have any directed acyclic graph, you can linearly order it.
And next time, what we're going to do is we're going to prove this theorem.
But not just that.
In addition to proving this theorem, we're actually going to make it algorithmic.
We're going to come up with an algorithm that,
given a DAG, actually produces this linear order.
So that's what you have to look forward to next time, and I'll see you then.
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