In this video, I'll give you a brief intro into the vast and incredibly interesting field of graph theory from a computer science perspective. Here's an example of a type of graph that we're going to talk about extensively in this video. If you were to ask me "What's the simplest definition of a graph for someone who knows nothing about the field?

" what I could tell you is that you can think of a graph as a network that helps define and visualize relationships between various components. In this example, the circles that you see represent the components, and the lines connecting them can be thought of as signifying a relationship between the components. These ideas of course have more formal names in graph theory.

We refer to these circles as vertices or nodes, and the relationships signified by connecting lines represent what we call edges in a graph. Graph theory is all about the study of the properties of these types of networks, and how they can be used to model and solve a whole host of interesting problems. This video is all about introducing you to core concepts related to graph theory, that we will then use to study fundamental graph algorithms.

We'll first start with an important discussion on why we should even care about studying graphs, after which we'll formally define a graph, and introduce important terminology used to communicate ideas about graphs. Then, we'll talk about how computers might represent graphs as a data structure. And then I'll finish the video off with some interesting problems and questions around graphs that you can think about going forward.

Let's first start with the most important question: "Why should we care about graph theory? " And the short answer to this is graphs show up everywhere; sometimes in expected ways, and also sometimes in surprising ways. One of the most direct applications are mapping and navigation applications.

In any of these applications, you often deal with roads and intersections, which can be naturally modeled as a graph, where each vertex represents an intersection, and the edge between vertices signify the roads between intersections. You could imagine, navigation applications might be interested in the best route between a starting point and an ending point; and this problem naturally translates into many well-known graph theory problems. Another natural application of graphs is in social networks.

In the context of this application, imagine edges of a graph now represent friendships between people in a network, where each node now represents an individual. Suppose you are interested in recommending new friends to person A, who currently has four friends. A natural way to solve this problem is to look at all of person A's friends, find friends of these friends, and recommend them.

These types of problems are easily modeled and solved with graph theory. So these are examples of applications where it's honestly not too surprising that something like graph theory shows up. But the neat thing about graph theory is that it also find ways to show up in the most unexpected places.

Let's talk about Sudoku. That's right. —I bet you didn't see that one coming.

A lot of you have probably encountered a Sudoku puzzle at some point, but for those of you who haven't, here's a quick summary of how this puzzle works, so we're all on the same page. The goal of Sudoku is to fill missing entries of a 9x9 grid with numbers 1–9, but with a few constraints: The first constraint is that each 3x3 subgrid cannot have repeated numbers. The next constraint is that each row must have unique entries.

And the last constraint puts a similar limitation on the columns, in which all entries of a column must be unique. It turns out that computers can solve Sudoku puzzles efficiently using graph theory. The graph here is quite subtle, but it does exist.

What we are going to do is we're gonna assign each number a color, and then construct a graph as follows: For each 3x3 grid, we'll fill out the known entries with the respective color for the number, and assign one color to all unknown entries. The constraint here is that all of these 9 nodes as a group must have unique colors. We can then extend this mapping to all other 3x3 grids, and create the following graph.

We still have to take care care of the other constraints. We can connect the graph along each row to represent the idea that every row on the graph must have a unique color. And we can do the same connections along columns for that respective constraint.

Now we have a graph theory problem where we attempt to find colors assigned to vertices that satisfy all the laid-out constraints. It turns out that this is actually a well-known graph problem that graph theory provides an elegant algorithm for, and once we find a set of colors, we have a solution to a Sudoku puzzle. Let's now proceed to formally define a graph.

A graph is a set of vertices and edges, where each edge is a connection between vertices. The way we usually denote an edge in a graph is by referring to it as a pair of vertices. As mentioned before, vertices and nodes are just different names for the same concept, that we will use interchangeably when discussing graphs.

If you're forced to mathematically write down a definition of a graph, we can use set notation. For this particular graph, the vertex set looks like V = {0, 1, 2, 3, 4}, and our set of edges would be denoted as E = {(0,1), (0,2), (0,3), (1,3), (2,3), (3,4)}. Let's now define some important terminology that you'll see over and over again when talking about graphs.

The first important term is the concept of neighbors in a graph. Formally, two vertices are neighbors if an edge connects them. Here's an example: Vertices 1 & 8 of this graph are neighbors, since they are connected by an edge.

One thing that we often query a graph for is all neighbors of a particular vertex. For example, if we were asked for all neighbors of node 0, the result would be the following set of 3 nodes: {4, 6, 8}. A related concept is the degree of a vertex.

A degree of specific vertex is equal to the number of edges connected to it, or equivalently, the number of neighbors. As per this definition, the degree of vertex 0 would be 3. And the degree of vertex 3 would be 2.

Let's now talk about paths. Paths are simply defined as a sequence of vertices connected by edges. Most of the paths that we'll deal with will be paths with unique vertices.

For example, one path from vertex 0 to 2 is 0→6→7→3→2, and all vertices in the path only show up once, which is what we'll assume most of the time. One feature of paths is that they have respective lengths. A path length is simply defined as the number of edges in the path.

In this example, the path length we have here would be 4. A related idea to a path is the concept of a cycle. A cycle is defined as a path that starts and ends at the same vertex.

One key note about cycles is that all cycles are paths, but not all paths are necessarily cycles. There are several cycles in this graph. Here's an example of a cycle that begins at vertex 0, and ends at vertex 0: 0→8→1→5→4→0 The last term I want to talk about is the concept of connectivity, which can be used in several contexts.

The first context is with respect to 2 specific vertices: two vertices are connected if a path exists between them. The second context you may encounter connectivity, is when it is applied to a general graph: a graph is connected when all vertices are pairwise connected. In other words, a path exists between all pairs of vertices.

This graph is an example of a connected graph, since if you pick any two vertices, we can identify a path between them. However, if we change up the graph a little bit, we now have an example of a graph that is not connected. Now it's easy to see that no path exists between several pairs of vertices.

This naturally leads to a third context in which connectivity can be applied, which is the idea of a connected component. A connected component is a subset of vertices of the graph that is connected. For example, in this graph we have two connected components: The first being the following set of vertices: V₁ = {0, 4, 6, 7, 8}, and then the remaining vertices of the graph form the second connected component.

Let's now transition to the types of graphs that you may encounter. The main graph we've seen so far is specifically called an undirected graph. Where for example if I have an edge connecting vertex 0 to vertex 1, it's implied that I also have an edge from vertex 1 to vertex 0.

A graph where this would not be the case is called a directed graph, where now edges are unidirectional. Directed graphs also have their own classes. This particular graph has a cycle, so we can be more specific by referring to this graph as a directed cyclic graph.

On the other hand, if a graph is directed and contains no cycles, we refer to that specifically as a directed acyclic graph, which is a specific subset of graphs that has been studied quite rigorously since they show up in all sorts of interesting problems. Another important graph is a weighted graph. This graph is unique because each edge now is not treated equally, and some edges might have a larger weight than others.

This can naturally model interesting metrics like traffic, distances on maps, and many other ideas. Another important class of graphs is trees. Trees have 3 key properties: all trees are connected and acyclic, removing an edge from a tree will disconnect the entire graph.

And furthermore, adding any single edge to a tree will create a cycle. These are 3 valid examples of trees, and I encourage you to take a second, pause the video, and confirm that these properties hold. Personally I find verifying the second and third properties surprisingly satisfying.

It really emphasizes how fragile a tree structure is, which I think, is kind of cool. Let's now move on to some more hands-on ideas. How does a computer represent a graph as a data structure?

Take for example the following graphs. How would you go about organizing the information in this graph on a computer? [It] turns out that there are several accepted ways to do this, and some of them are better than others depending on the context.

The first idea that is quite natural is to map vertices to one another through a matrix, which we formally call an adjacency matrix. The rules for creating this matrix are fairly intuitive: if an edge between node i and j exists, we will indicate this with an entry 1 in our matrix; otherwise, the entry will be 0. Following these rules, this is what our adjacency matrix looks like for this graph.

Notice that each edge creates two entries since this is an undirected graph. Make sure you take a moment to understand the mapping we have here. The second valid representation of a graph is actually fairly simple.

We take all the edges and construct a set with each edge as follows. This representation is called an edge set, and it also contains all the information about vertices and edges that you would need for a graph. However, this representation is not as common, because it's a little hard to extract information about vertices of a graph using this particular representation.

The third representation of a graph is called an adjacency list, and this is actually the most common representation used. The idea works as follows: we take each vertex, and map it to a list of its neighbors. For example, node 0 has 3 neighbors, specifically nodes 1, 2 and 3, so we map it to a list containing those values.

The rest of the list is constructed in a similar manner. [The] nice thing about this representation is that it gives us an easy access to neighbors of a particular node, which is a tool that will be immensely useful in graph algorithms. Furthermore, this representation exploits the fact that most graphs in the real world are going to be sparse, meaning that we have a large number of vertices, with each vertex having relatively few edges.

For example, in a social network you would actually make the most sense to have this representation, since there are going to be billions of nodes, but each node is unlikely to have more than a few thousand edges. An adjacency matrix for a graph like that would take way too much memory, but with an adjacency list it's much more manageable. For this reason in future videos we will primarily use adjacency lists as a way to represent graphs.

The final thing I want to do in this video is to discuss some of the interesting problems and questions you may find in graph theory. In future videos we'll show you how you can apply graph algorithms to solve many of these problems. A fairly standard problem that you're likely to encounter all the time revolves around connectivity between two vertices.

For example, it's easy for us to see that vertices 0 and 3 have many paths between them. But what sort of algorithms can we use to allow a computer to efficiently solve this problem? We can naturally extend this problem to ask if an entire graph is connected.

There are a variety of efficient algorithms to solve these problems that we will cover in the next few videos. Another interesting problem in graph theory is the shortest path problem. Specifically, what is the path of the least length between two vertices?

For this particular graph, here's the shortest path between vertices 0 and 3, which is something we as humans can generally eyeball for small enough graphs; but once again, what's the algorithm to solve this problem generally? Then there is the problem of cycle detection in a graph. Many of the algorithms that are used for connectivity problems can be naturally adapted to solve this problem as well.

Another interesting problem in graph theory has to do with vertex coloring, which we actually saw in the Sudoku example. The problem formally stated is: given a set of colors, can we assign a color to each vertex such that no two neighbors are assigned the same color? In this graph, for two colors no such solution exists, but if given 3 colors, this problem has a solution.

Another very interesting problem to ask about graphs is whether a path exists that uses every edge exactly once. This might be harder to tell, but this graph does indeed have one of these paths, and an efficient algorithm for this problem exists. These types of paths have tons of interesting applications in bioinformatics and circuit design.

A similar question that you may ask is what about the existence of a path that uses every vertex exactly once? Such path also exists in this graph, but I think what you'll find more surprising is that there exist no efficient algorithms to solve this problem. What I mean by that is that all algorithms we have so far run in exponential time for this problem, which is incredibly slow for large enough graphs.

In fact, if you are able to find an efficient algorithm for this particular problem, you would probably be given every single computer science award in existence, because it would help resolve a long-standing open problem. But don't get your hopes up there, because there's a fairly large consensus that no efficient algorithm for this problem exists. Let's now take a moment to recap the big ideas in this video.

We first introduced graph theory through a variety of cool applications, where you saw why someone should even study graph theory. We then formally define graph theory along with important terminology and types of graphs that you will encounter. These concepts are incredibly important to understand, so that we have an appropriate framework for discussing graphs.

We then introduced several representations of a graph as a data structure, with a specific emphasis on the practicality of the adjacency list. And lastly, we introduced some diverse and interesting problems to motivate the algorithms that we will go through in future videos. I hope this discussion gave you an appreciation for the massive field of graph theory.

It's definitely one of my favorite topics in computer science, and I hope it becomes yours as well. Thanks for watching me. And as always, I'd appreciate it if you hit the like button if you enjoyed it.

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Introduction to Graph Theory: A Computer Science Perspective