# Difference between revisions of "Graph Theory"

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== Adjacency Matrices == | == Adjacency Matrices == | ||

− | It is frequently convenient to represent a graph by a matrix known as | + | It is frequently convenient to represent a graph by a matrix known as an ''adjacency matrix.'' Consider |

+ | the following directed graph: | ||

[[File:DirectedGraph.png|200px]] | [[File:DirectedGraph.png|200px]] | ||

− | To draw adjacency matrix, we | + | To draw the adjacency matrix, we create an $N$ by $N$ grid and label the rows and columns for each vertex (diagram at the left). Then, place a 1 for each edge in the cell whose row and column correspond to the |

+ | starting and ending vertices of the edge (diagram in the middle). Finally, place | ||

+ | a 0 in all other cells (diagram at the right). | ||

[[File:AdjMatrix.png|600px]] | [[File:AdjMatrix.png|600px]] | ||

− | By construction, | + | By construction, cell $(i,j)$ in the matrix with a value of 1 indicates a direct path from vertex $i$ to vertex $j$. |

+ | If we square the | ||

+ | matrix, the value in cell $(i,j)$ indicates the number of paths of length 2 from vertex $i$ to vertex $j$. | ||

[[File:AdjMatrix2.png|250px]] | [[File:AdjMatrix2.png|250px]] | ||

− | + | For example, the $M^2$ says that there 2 paths of length 2 from A to C (A → B → C and A → D → C). This also says that there is exactly 1 path of length 2 from A to D (A → B → D), exactly 1 path of length 2 from B to B (B → C → B), and so on. | |

− | + | In general, if we raise $M$ to the ''pth'' power, the resulting matrix indicates which paths of length $p$ exist in the graph. | |

+ | The value in $M^p(i,j)$ is the number of paths from vertex $i$ to vertex $j$. | ||

== Sample Problems == | == Sample Problems == |

## Latest revision as of 13:49, 1 September 2020

Many problems are naturally formulated in terms of points and connections between them. For example, a computer network has PCs connected by cables, an airline map has cities connected by routes, and a school has rooms connected by hallways. A graph is a mathematical object which models such situations.

## Contents

## Overview

A *graph* is a collection of vertices and edges. An *edge* is a connection between two *vertices* (sometimes referred to as *nodes*). One can draw a graph by marking points for the vertices and drawing lines connecting them for the edges, but the graph is defined independently of the visual representation. For example, the following two drawings represent the same graph:

The precise way to represent this graph is to identify its set of vertices {A, B, C, D, E, F, G}, and its set of edges between these vertices {AB, AD, BD, CF, FG, GH, GE, HE}.

## Terminology

The edges of the above graph have no directions meaning that the edge from one vertex A to another vertex B is the same as from vertex B to vertex A. Such a graph is called an *undirected graph*. Similarly, a graph having a direction associated with each edge is known as a *directed graph*.

A *path* from vertex x to y in a graph is a list of vertices, in which successive vertices are connected by edges in the graph. For example, FGHE is path from F to E in the graph above. A *simple path* is a path with no vertex repeated. For example, FGHEG is not a simple path.

A graph is *connected* if there is a path from every vertex to every other vertex in the graph. Intuitively, if the vertices were physical objects and the edges were strings connecting them, a connected graph would stay in one piece if picked up by any vertex. A graph which is not connected is made up of *connected components*. For example, the graph above has two connected components: {A, B, D} and {C, E, F, G, H}.

A *cycle* is a path, which is simple except that the first and last vertex are the same (a path from a point back to itself). For example, the path HEGH is a cycle in our example. Vertices must be listed in the order that they are traveled to make the path; any of the vertices may be listed first. Thus, HEGH and EHGE are different ways to identify the same cycle. For clarity, we list the start / end vertex twice: once at the start of the cycle and once at the end.

We’ll denote the number of vertices in a given graph by V and the number of edges by E. Note that E can range anywhere from $V$ to $V^2$ (or $V(V-1)/2$ in an undirected graph). Graphs with all edges present are called *complete graphs*; graphs with relatively few edges present (say less than $V log(V)$) are called *sparse*; graphs with relatively few edges missing are called *dense.*

## Directed Graphs

*Directed graphs* are graphs which have a direction associated with each edge. An edge xy in a directed graph can be used in a path that goes from x to y but not necessarily from y to x. For example, a directed graph similar to our example graph is drawn below:

This graph is defined as the set of vertices V = {A,B,C,D,E,F,G,H} and the set of edges {AB,AD,DA,DB,EG,GE,HG,HE,GF,CF,FC}. There is one directed path from G to C (namely, GFC); however, there are no directed paths from C to G. Note that a few of the edges have arrows on both ends, such as the edge between A and D. These dual arrows indicate that there is an edge in each direction, which essentially makes an undirected edge. An *undirected graph* can be thought of as a directed graph with all edges occurring in pairs in this way. A directed graph with no cycles is called a *dag* (directed acyclic graph).

## Trees & Forests

A graph with no cycles is called a *tree*. There is only one path between any two nodes in a tree. A tree with $N$ vertices contains exactly $N-1$ edges.

The two graphs shown above are trees because neither has any cycles and all vertices are connected. The graph on the left has 4 vertices and 3 edges; the graph on the right has 8 vertices and 7 edges. Note that in both cases, because they are trees, the number of edges is one less than the number of vertices.

A group of disconnected trees is called a *forest*.

A *weighted graph* is a graph that has a weight (also referred to as a cost) associated with each edge.
For example, in a graph
used by airlines where cities are vertices and edges are cities with direct flights connecting them,
the weight for each edge might be the distance between the cities.

A *spanning tree* of a graph is a subgraph that contains all the vertices and forms a tree.
A *minimal spanning tree*
can be found for weighted graphs
in order to minimize the cost across an entire network.

## Adjacency Matrices

It is frequently convenient to represent a graph by a matrix known as an *adjacency matrix.* Consider
the following directed graph:

To draw the adjacency matrix, we create an $N$ by $N$ grid and label the rows and columns for each vertex (diagram at the left). Then, place a 1 for each edge in the cell whose row and column correspond to the starting and ending vertices of the edge (diagram in the middle). Finally, place a 0 in all other cells (diagram at the right).

By construction, cell $(i,j)$ in the matrix with a value of 1 indicates a direct path from vertex $i$ to vertex $j$. If we square the matrix, the value in cell $(i,j)$ indicates the number of paths of length 2 from vertex $i$ to vertex $j$.

For example, the $M^2$ says that there 2 paths of length 2 from A to C (A → B → C and A → D → C). This also says that there is exactly 1 path of length 2 from A to D (A → B → D), exactly 1 path of length 2 from B to B (B → C → B), and so on.

In general, if we raise $M$ to the *pth* power, the resulting matrix indicates which paths of length $p$ exist in the graph.
The value in $M^p(i,j)$ is the number of paths from vertex $i$ to vertex $j$.

## Sample Problems

### Problem 1

Find the number of different cycles contained in the directed graph with vertices {A, B, C, D, E} and edges {AB, BA, BC, CD, DC, DB, DE}.

**Solution:**
The graph is as follows:

By inspection, the cycles are: ABA, BCDB, and CDC. Thus, there are 3 cycles in the graph.

### Problem 2

In the following directed graph, find the total number of different paths from vertex A to vertex C of length 2 or 4.

**Solution:**

Let matrix $M$ represent the graph. Recall that the number of paths from vertex $i$ to vertex $j$ of length $p$ equals $M^p[i,j]$. The values of $M$, $M^2$ and $M^4$ are:

There is 1 path of length 2 from A to C (cell [1,3] in $M^2$). By inspection, the only path of length 2 is A → A → C.

There are 3 paths of length 4 (cell [1,3] in $M^4$) and they are A → A → A → A → C, A → A → C → A → C, A → C → A → A → C.

### Problem 3

Given the adjacency matrix, draw the directed graph.

**Solution:**

There must be exactly 4 vertices: V = {A, B, C, D}. There must be be exactly 7 edges: E = {AB, AD, BA, BD, CA, DB, DC}. Here are two valid drawings of the graph:

## Video Resources

### ACSL Videos

The following YouTube videos show ACSL students and advisors working out some ACSL problems that have appeared in previous contests. Some of the videos contain ads; ACSL is not responsible for the ads and does not receive compensation in any form for those ads.

ACSL Math: Graph Theory (Raj Joshi)
This video introduces the topic, then using an example problem, explains the methodology to solve problems that appear on ACSL contests. | |

ACSL - Graph Theory Worksheet 1 (misterminich)
Shows the solution to the ACSL problem: ' | |

ACSL Graph Theory Worksheet 3 (misterminich)
Shows the solution to an ACSL problem asking to find how many paths of a specific length there are in a specific directed graph. | |

Graph Theory ACSL Example Problem (Tangerine Code)
Shows the solution to an ACSL problem asking to find how many different cycles there are from a specific vertex in a specific directed graph. |

ACSL Test Prep - Graph Theory (Mrs. Goopta)
A talked-over presentation discussing graph theory as needed for the American Computer Science League and its tests. |

### Other Videos

There are dozens of YouTube videos that provide an introduction to graph theory. The following are a couple that we found particularly well done. Be aware that the videos contain ads; ACSL is not responsible for the ads and does not receive compensation in any form for those ads.

CS 106B Lecture: Graphs: basic concepts
This lecture from Stanford University is s wonderful introduction to graph theory. The lecture starts out with many examples of real world problems that are modeled by graphs, and then moves on to review basic terminology relating to graph theory. | |

Data structures: Introduction to Graphs (mycodeschool)
A nice introduction to the fundamental concepts of graphs. |