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Introduction to Network Mathematics provides college students with basic terminology of graph theory for the purpose of better understanding the Internet. Many passages are edited from Wikipedia, a few are from PlanetMath, and others are original writing by Bruce Hoppe, who teaches CS103: "Introduction to Internet Technologies and Web Programming" at Boston University. This is a work in progress being created in the spring semester of 2007.
Copyright (c) 2007 by Bruce Hoppe: Permission is granted to copy, distribute and/or modify these documents under the terms of the GNU Free Documentation License, Version 1.2 or any later version published by the Free Software Foundation; with no Invariant Sections, with no FrontCover Texts, and with no BackCover Texts. A copy of the license is included in the section entitled "GNU Free Documentation License". If you believe a portion of this site infringes on your or anyone's copyright, please send us a note.
Contents
 Sets
 Explicit and implicit set notation
 Cardinality
 Subsets
 Unions, intersections, and complements
 Ordered pairs and ordered lists
 Partitions
 Graphs
 Undirected and directed graphs
 Adjacent and Incident
 Degree
 Density and average degree
 Neighborhood
 Paths and walks
 Connected components
 Length, distance, diameter
 Cliques and independent sets
 Clustering
 Network Structure
 Bipartite (affiliation) graphs
 Trees and forests
 Minimum spanning trees
 Kruskal's algorithm
 Google ranking and centrality
Sets
A set is a collection of objects. These objects are called the elements or members of the set. Objects can be anything: numbers, people, other sets, etc. For instance, 4 is a member of the set of all even integers. Clearly, the set of even integers is infinitely large; there is no requirement that a set be finite.
If x is a member of A, then we write x A. We may also say that x "belongs to" A, or that x "is in" A.
If x is not a member of A, then we write x A.
Two sets A and B are defined to be equal when they have precisely the same elements, that is, if every element of A is an element of B and every element of B is an element of A. Thus a set is completely determined by its elements; the description is
immaterial. For example, the set with elements 2, 3, and 5 is equal to
the set of all prime numbers less than 6.
If the sets A and B are equal, then we write A = B.
We also allow for an empty set, a set without any members at all. We write the empty set as { }.
Explicit and Implicit Set Notation
Explicit Set Notation: The simplest way to describe a set is to list its elements between curly braces, known as defining a set explicitly. Thus {1,2} denotes the set whose only elements are 1 and 2. Note the following points:
 Order of elements is immaterial: for example, {1,2} = {2,1}.
 Repetition of elements is irrelevant: for example, {1,2,2} = {1,1,1,2} = {1,2}.
Warning: This notation can be informally abused by saying something like {dogs} to indicate the set of all dogs, but this example would be read by mathematicians as "the set containing the single element dogs".
The simplest possible example of explicit set notation is { }, which denotes the empty set.
Implicit Set Notation: We can also use the notation {x : P(x)} to denote the set containing all objects for which the condition P holds, known as defining a set implicitly. For example, {x : x is a real number} denotes the set of real numbers, {x : x has blonde hair} denotes the set of everything with blonde hair, and {x : x is a dog} denotes the set of all dogs.
Cardinality
Given a finite set A, the cardinality of A is simply the number of elements in A. We write the cardinality of A as A, which we may read as "cardinality of A" or "size of A." Note that this verticalbar notation is the same as used to denote absolute value, but the meaning of cardinality is quite different from absolute value. In particular, absolute value operates on numbers (e.g., 3 = 3) while cardinality operates on sets (e.g., {3} = 1). Other examples of cardinality are: {3,4} = 2.
 { } = 0. The empty set has no elements.
 {{1,2},{3,4}} = 2. In this case the two elements of {{1,2},{3,4}} are themselves sets: {1,2} and {3,4}.
 {x: x is registered for CS103} = the number of people registered for CS103.
Subsets
Given two sets A and B we say that A is a subset of B if every element of A is also an element of B. Notice that in particular, B is a subset of itself; a subset of B that isn't equal to B is called a proper subset.
If A is a subset of B, then one can also say that B is a superset of A, that A is contained in B, or that B contains A. In symbols, A ⊆ B means that A is a subset of B, and B ⊇ A means that B is a superset of A. For clarity, we use "⊆" and "⊇" for subsets (which allow set equality) and we use "⊂" and "⊃" are reserved for proper subsets (which exclude equality).
For example, let A be the set of American citizens, B be the set of
registered students at Boston University, and C be the set of
registered students in CS103. Then C is a proper subset of B and B
contains C, which we can write as C ⊂ B and B ⊃ C. If we allow the possibility that every BU student might be registered for CS103, then we would write C ⊆ B or B ⊇ C
to allow for this unlikely situation. Note that not all sets are
comparable in this way. For example, it is not the case either that A is a subset of B nor that B is a subset of A.
Unions, intersections, and relative complements
Given two sets A and B, the union of A and B is the set consisting of all objects which are elements of A or of B or of both. It is written as A ∪ B.
The intersection of A and B is the set of all objects which are both in A and in B. It is written as A ∩ B.
Finally, the relative complement of B relative to A, also known as the set theoretic difference of A and B, is the set of all objects that belong to A but not to B. It is written as A \ B. Symbolically, these are respectively
Notice that B doesn't have to be a subset of A for A \ B to make sense.
To illustrate these ideas, let A be the set of lefthanded people, and let B be the set of people with blond hair. Then A ∩ B is the set of all lefthanded blondhaired people, while A ∪ B is the set of all people who are lefthanded or blondhaired or both. A \ B, on the other hand, is the set of all people that are lefthanded but not blondhaired, while B \ A is the set of all people that have blond hair but aren't lefthanded.
Venn diagrams are illustrations that show these relationships pictorally. The above example can be drawn as the following Venn diagram:
Disjoint sets: Let E be the set of all human beings, and let F be the set of all living things over 1000 years old. What is E ∩ F in this case? No human being is over 1000 years old, so E ∩ F must be the empty set { }. When the intersection of any two sets is empty, we say those two sets are disjoint.
Ordered Pairs and Ordered Lists
Intuitively, an ordered pair is simply a collection of two objects such that one can be distinguished as the first element and the other as the second element, and having the fundamental property that two ordered pairs are equal if and only if their first elements are equal and their second elements are equal.
We write an ordered pair using parenthesesnot curly braces, which are used for sets. For example, the ordered pair (a,b) consists of first element a and second element b.
Two ordered pairs (a,b) and (c,d) are equal if and only if a = c and b = d.
In contrast, a set of two elements is an unordered pair which we write using curly braces and where there is no distinction between the first element and the second element. It follows that Two ordered pairs (a,b) and (c,d) are equal if and only if a = c and b = d
 Two sets {a,b} and {c,d} are equal if and only if
 Either a = c and b = d
 Or a = d and b = c
 (1,2,3) is an ordered list. Note that (1,2,3) is different from (3,2,1).
 {1,2,3} is a set. Note that {1,2,3} = {3,2,1}.
 {(1,2),(3,4)} is a set of two elements, each of which is an ordered pair. One of the elements of the set is (1,2) and the other element of the set is (3,4).
 ({1,2},{3},{2,4}) is an ordered list of sets. The first element of the list is the set {1,2}; the second element of the list is the set {3}; the third element of the list is the set {2,4}.
Partitions
A partition of U into 6 subsets:
a Venn diagram representation.
A partition of a set X is a division of X into nonoverlapping subsets such that every element of X is in exactly one of these subsets.
Equivalently, a set P of subsets of X, is a partition of X if
 The union of the elements of P is equal to X.
 The intersection of any two elements of P is empty.
We also typically require that each subset of a partition be nonempty. For example:
 If A is the set of all integers, E is the set of even integers, and O is the set of odd integers, then {E,O} is a parition of A.
 If B is the set of all registered BU students and C is the set of all registered CS103 students, then {C,B\C} is a partition of B.
 For any set X, P = {X} is a partition of X.
 Every singleton set {x} has exactly one partition, namely { {x} }.
 The set { 1, 2, 3 } has these five partitions:
 { {1}, {2}, {3} }
 { {1, 2}, {3} }
 { {1, 3}, {2} }
 { {1}, {2, 3} }
 { {1, 2, 3} }
Graphs
Mathematicians have developed graph theory in order to study all kinds of networks. A graph is a set of objects connected by lines.The objects in a graph are usually called nodes or vertices. The lines connecting the objects are usually called links or edges.
More formally, we define a graph G as an ordered pair G = (V,E) where
 V is a set of vertices (nodes)
 E is a set of edges (links).
 Each edge is a pair of vertices. In other words, each element of E is a pair of elements of V.
Example: The picture above represents the following graph:
 V = {1,2,3,4,5,6}
 E = {{1,2},{1,5},{2,3},{2,5},{3,4},{4,5},{4,6}}
Trivial and empty graphs: The graph with only one vertex and no edges is called the trivial graph. The graph with no vertices and no edges is called the empty graph.
Undirected and Directed Graphs
Undirected graph: The edges of a graph are assumed to be unordered pairs of vertices. Sometimes we say undirected graph to
emphasize this point. In an undirected graph, we write edges using
curly braces to denote unordered pairs. For example, an undirected edge
{2,3} from vertex 2 to vertex 3 is the same thing as an undirected edge {3,2} from vertex 3 to vertex 2.
Directed graph: In a directed graph (or digraph for short), the two directions are counted as being distinct directed edges. In an directed graph, we write edges using parentheses to denote ordered pairs. For example, edge (2,3) is directed from 2 to 3, which is different than the directed edge (3,2) from 3 to 2. Directed graphs are drawn with arrowheads on the links.
Adjacent and Incident
Two edges of a graph are called adjacent if they share a common vertex. Similarly, two vertices are called adjacent if they share a common edge, in which case the common edge is said to join the two vertices.
An edge and a vertex on that edge are called incident.
Degree
The degree of a vertex is the number of edges incident to the vertex. The degree of a vertex v is denoted deg(v).For an undirected graph, the degree of a vertex is the number of edges incident to the vertex.
The graph above has the following degrees:Vertex  Degree 

1  2 
2  3 
3  2 
4  3 
5  3 
6  1 
The indegree of a vertex v is denoted deg ^{+} (v) and the outdegree as deg ^{−} (v)
The digraph above has the following degrees:
Vertex  Indegree  Outdegree 

1  0  2 
2  2  0 
3  2  2 
4  1  1 
Density and Average Degree
The graph density of a graph G = (V,E) measures how many edges are in set E compared to the maximum possible number of links between vertices in set V. Density is calculated as follows:
 An undirected graph with no loops can have at most V * (V − 1) / 2 edges, so the density of an undirected graph is 2 * E / (V * (V − 1)).
 A directed graph with no loops can have at most V * (V − 1) edges, so the density of a directed graph is E / (V * (V − 1))
Neighborhood
The neighborhood of a vertex v in a graph G is the set of vertices adjacent to v. The neighborhood is denoted N(v). The neighborhood does not include v itself, and is more specifically the open neighborhood of v. We also define a neighborhood in which v itself is included, called the closed neighborhood and denoted by N[v]. When stated without any qualification, a neighborhood is assumed to be open.
Neighborhoods are also used in the clustering coefficient of a graph, which is a measure of the average density of its neighborhoods.
Paths and Walks
A path P = (v_{1},v_{2},v_{3},...,v_{n}) is an ordered list of vertices such that
 From each of its vertices there is an edge to the next vertex in the list
 No vertex occurs in the list more than once.
We say walk to denote an
ordered list of vertices that follows the first requirement above but
not the second. (In other words, a walk may visit the same vertex more
than once, but a path must never do so.) The first vertex is called the
origin and the last vertex is called the destination. Both origin and destination are called endpoints of the path. The other vertices in the path are internal vertices. A cycle
is a walk such that the origin and destination are the same; a simple cycle is a cycle that does not repeat any vertices other than the origin and destination.
The same concepts apply both to undirected graphs and directed graphs, with the edges being directed from each vertex to the following one. Often the terms directed path and directed cycle are used in the directed case.
Example: The following
drawing represents a directed cycle. It is not a simple cycle because
the blue nodes are visited more than once. Without the arrows, it is
an (undirected) cycle.
Connected Components
A connected graph is one where each vertex can reach every other vertex by one or more paths. A connected graph is said to consist of a single connected component. If a graph is not connected, then it consists of two or more connected components. Intuitively, connected components are easy to see:
More formally, a connected component is a subgraph with the following two properties:
 Each vertex in the subgraph can reach every other vertex in the subgraph by one or more paths
 There is no edge from any vertex inside the subgraph to any vertex outside the subgraph. (This means the subgraph can't get any bigger without losing connectivity; it is maximal.)
Length, Distance, Diameter
The length of a path or walk is the number of edges that it
uses, counting multiple edges multiple times. In the graph below, (5, 2, 1) is a path of
length 2 and (1,
2, 5, 1, 2, 3) is a walk of length 5.
The distance between two vertices x and y is written d(x,y) and defined as the length of the shortest path from x to y. If there is no path from x to y (i.e., if x and y are in different connected components), then d(x,y) is infinity.
The diameter of a graph G is the maximum distance between any two vertices in G. If G is not connected, then the diameter of G is infinity.
Cliques and Independent Sets
In a complete graph, every pair of vertices is joined by an edge.
Given a graph, a clique is a subset of vertices such that every pair of vertices in the subset is joined by an edge. The size of a clique is the number of vertices it contains. The red nodes {6,8,12} below are a clique of size three:
It is an interesting and very hard problem to
find the largest clique in a graph known as the maximum clique problem. Can you find the largest clique in the graph above?
The opposite of a clique is an independent set. An independent set is a subset of vertices such that for every two vertices in the subset, there is no edge
connecting the two. The size of an independent set is the
number of vertices it contains.
The vertices of any edgeless graph make an independent set, rather trivially:
Given an arbitrary graph (with edges), it is an interesting and hard problem to find
the largest independent set in the graph.
This is known as the maximum independent set problem. The blue nodes below are a maximum independent set:
Clustering
The clustering coefficient C(v) for a vertex v measures
the extent to which neighbors of v are also neighbors of each other.
More precisely, it is the proportion of links between the vertices
within the neighborhood of v divided by the number of links that could
possibly exist
between them. For a undirected graph, there are (1/2) * deg(v) * (deg(v)  1) links that could exist among the vertices within the neighborhood of v. Thus, the clustering coefficient is given as:2 * { {x,y} : xN(v) and yN(v) and {x,y}E }
C(v) = 
deg(v) * (deg(v)  1)
This measure is 1 if every neighbor of v is also joined to every other neighbor of v, and 0 if no neighbor of v is joined to any other neighbor of v.
The clustering coefficient for the whole graph is the average of the clustering coefficient for each
vertex
Network Structure
Bipartite (Affiliation) Graphs
A bipartite graph (also called affiliation graph in Six Degrees) is a graph where the vertices can be partitioned into two independent sets.
A more intuitive way to think of a bipartite graph is as a graph whose nodes can each be colored
red or blue such that there are no redred edges and no blueblue edges.
Another way to think of a bipartite graph is by drawing the red
nodes on one side of the picture and the blue nodes on the other side of the picture:
Bipartite graphs are useful for modelling matching problems. For example, which actors have worked on which movies? Which websites are bookmarked by which people? These are bipartite graphs.
Most graphs are nonbipartite. A graph is nonbipartite when there is:
 No way to partition its vertices into two independent sets
 No way to color its vertices red and blue without creating redred or blueblue edges
 No way to draw its vertices in two columns so that edges only cross from column to column and never join vertices in the same column
Suppose we color the first node red; then we must color the second node
blue. But then there is no way to color the third node red or blue
without creating either a redred edge or a blueblue edge.
Social Bookmarking, Tagging, and Bipartite Networks
One application of bipartite networks is the organization of
usercreated tags, such as the tags in del.icio.us.
Together with the website
URLs they are associated with, these tags form a bipartite network. For
example, the yellow nodes below left are tags on del.icio.us, and the
orange nodes below right are some of the URLs associated with these
tags:
Node similarity coefficient
Even one that doesn't understand the tagwords themselves (say, because it's a machine) can analyze how the tagwords
relate to each other based on how others associate those tags with website URLs.
The simplest measurement of how closely related two tags are is based on comparing
their network neighborhoods. Essentially, two nodes (or tags) with similar neighborhoods
are “close” to each other; two nodes with different neighborhoods are “far”
from each other.
In the example above, the neighborhoods of YouTube and Video have many nodes in common and so they are "close." By contrast, the neighborhoods of MySpace and Video have no nodes in common and so they are "far."
We formalize this notion by defining the node similarity coefficient:
similarity(x,y) = N(x) ∩ N(y) / N(x) ∪ N(y)
This equals 1 when two nodes have identical neighborhoods and
0 when two nodes have mutually exclusive neighborhoods.
Taking an equivalent (but opposite) tack, we define the node difference coefficient by subtracting the above similarity coefficient from 1:
difference(x,y) = 1 – (N(x) ∩ N(y) / N(x) ∪ N(y))
The difference coefficient is therefore 0 when two nodes have identical neighborhoods and 1 when two nodes have mutually exclusive neighborhoods.
The node difference coefficient gives us a notion of distance which
is very useful; however, we must be careful to be clear when we are
using "distance" to mean the length of shortest paths (as we have
previously defined
“distance”) vs when we use the node difference coefficient as a notion
of distance.
Examples:
The node difference coefficient for YouTube and Video is
1 – (N(YouTube) ∩ N(Video) / N(YouTube) ∪ N(Video))
= 1  (4 / 6)
= 1/3
The node difference coefficient for MySpace and Video is
1 – (N(MySpace) ∩ N(Video) / N(MySpace) ∪ N(Video))
= 1  (0 / 9)
= 1
Can you calculate the node difference coefficients for each of the other pairs of tags above?
Node Difference Coefficients 
Youtube 
Video 
Music 
MySpace 
Web2.0 
Youtube 
0 
1/3 



Video 
1/3 
0 

1 

Music 


0 


MySpace 

1 

0 

Web2.0 




0 
Trees
A tree is a graph in which any two vertices are connected by exactly one path.Here are three different trees made from the same set of nodes:
Rooted (or Hierarchical) Trees
In computer science, we often use trees for hierarchical organization of data, in which case we use rooted trees.
In the above tree, node 3 is specially designated as the root of the tree. Every other node in the tree is implicitly ranked by its distance from the root.
For any edge in a rooted tree, one end of the edge must be
closer to the root than the other. We call the node closer to the root
the parent of the other node, and we call the node farther from the root a child of its parent node. For example, nodes 9 and 4 are children of
node 3. Similarly, nodes 10, 6, and 5 are all children of node 4. (And node 2 is
the only child of node 9.) Any node in a rooted tree may have any
number of children (including zero). A node with no children is called
a leaf (examples leaf nodes are 1, 10, 7, and 5). Every node except the root node must have exactly one parent.
For example, the parent of node 7 is node 6. Note that the above rooted
tree is undirected, but each edge in the rooted tree has an implicit
direction based on the parentchild relationship represented by that
edge.
Any node of a tree can be a valid root of that tree. For example, if
we decided that node 6 was actually the central anchor of the above
network, we could reroot the exact same tree at 6 instead of 3:
Check
and see that the two graphs above have exactly the same sets of nodes
and edges. What happens to parentchild relationships when a tree is
rerooted?
In terms of its graph theoretic properties, a rooted tree is equivalent to the traditional hierarchical organizational chart:
The Division Officer at the top of the above hierarchy would probably
argue that changing the root of the corresponding tree is a bad idea. However,
if we are using trees to connect computers or concepts rather than
people, we are less likely to encounter resistance when considering
different alternatives for designating the root of a tree.
Important properties of trees
A tree (rooted or not) may also be defined by the following equivalent conditions:Definition #1 above gives us an easy way to eyeball a graph and see if it is a tree. Does the graph consist of one connected component or does it fall apart into several connected components? A tree must be connected. Does the graph have any cycles (intuitively, a path that ends where it started)? A tree cannot have any cycles.
Which of the following three graphs are trees or not trees?
Definition #2 above gives us a precise relationship between the number of nodes and edges in a tree, which can be very useful when designing algorithms that involve trees.
It is somewhat remarkable that these three conditions are completely equivalent:
 G is a connected graph with no cycles.
 G is a connected graph with E = V  1.
 G is a graph in which any two vertices are connected by exactly one path.
Mathematical InductionWhen you write a computer program, how do you know it will work? A mathematical proof is a rigorous explanation of such a claim. (Example claim: "MapQuest finds the shortest path between any two addresses in the United States.") A proof starts with statements that are accepted as true (called axioms) and uses formal logical arguments to show that the desired claim is a necessary consequence of the accepted statements.Mathematical induction (or simply induction) is one of the most important proof techniques in all of computer science. Induction is a systematic way of building from small example cases of a claim to show that the claim is true in general for all possible examples. Every proof by induction has two parts:
Suppose we want to prove that the maximum possible number of edges in an undirected graph with n nodes is n*(n1)/2. Let's prove this by induction:
The maximum number of edges incident to node i+1 is simply i (which would happen if node i+1 is adjacent to every other node in the graph). Adding the above two terms gives us the maximum number of edges in a graph with n=i+1 nodes equal to
Voila! Check for yourself that we have completed both the base case and the inductive step. We now have a complete proof by induction that the maximum number of edges in a graph with n nodes is n*(n1)/2. 
Now we return to our original goal, to understand better why these three conditions are equivalent:
 G is a connected graph with no cycles.
 G is a connected graph with E = V  1.
 G is a graph in which any two vertices are connected by exactly one path.
 Base Case: Consider a graph with one node. A onenode graph is trivially connected and has no cycles. It cannot have any edges. We immediately get E = V  1 = 0.
 Inductive Step: Consider any connected graph with no cycles and no more than i nodes. The inductive hypothesis lets us assume that the number of edges is one less than the number of nodes for any such graph. Now consider a connected graph with no cycles and i+1 nodes. We aim to show that any such graph has exactly i edges, or that E = V  1 = (i+1)1 = i.
Because the graph is connected, there is at least one edge from node i+1 to the rest of the graph. There may be more than one edge from node i+1 to the rest of the graph. Below we draw the case where there are three such edges:
To illustrate why this is so, we have colored the edges incident to node i+1 in the above example along with the subtrees they connect to. Notice how each of the three "NO" scenarios violates one of our assumptions, either by creating a cycle (which either of the two red dashed edges would do) or by creating a disconnected graph (which the grey node would do).
Reiterating: However many edges are incident to node i+1, then the nodes 1, 2, 3, ..., i break up into exactly that many subtrees when we remove node i+1 and its edges.
Because each of the subtrees has no more than i nodes, we can use the inductive hypothesis to tell us that each subtree has exactly one less edge than it has nodes.
In the above example, this means
# edges in blue subtree =
# edges in green subtree = # edges in yellow subtree =  V_{blue}  1 V_{green}  1 V_{yellow}  1 
Although we don't know how many nodes are in each subtree, we do know the total number of nodes in all the subtrees is i. So if we add the three equalities above, we get
# edges in all subtrees =
 i  3 
E =
 i  3 + 3 = i 
Three is a number we arbitrarily chose for the sake of this exampleand notice how the 3's cancel each other out in the above sum. No matter whether there are 3, 2, 1, or whatever number of edges incident to node i+1, when we look at a connected graph with no cycles and i+1 nodes, we will end up with E = i, just as we aimed to prove. This completes our proof by induction that a connected graph with no cycles has E = V  1.
Minimum cost trees
Suppose your new office building is being wired for Internet. For the service to be worth much, your network must be connected (a path between every pair of computers); however, you probably don't want to install direct cables connecting every possible pair of computers.Trees provide a useful starting point for any network service provider facing this sort of problem. This is because a tree connects a network with the minimum possible number of edges. (Think about why this is true.)
Simply finding any tree to connect a network isn't enough though. Not all computers (or nodes) are equally costly to join directly. Consider the network below:
There are numbers next to each edge. Each number represents the cost (or weight) of the edge. In the above example, the weight of each edge corresponds to how far apart its endpoints are.
Suppose we want to choose a subset of edges to connect the above 7 nodes in a tree. Any tree connecting 7 nodes must have exactly 6 edges, so finding a "minimum edge tree" is meaningless. What we want are the 6 edges that form a tree for the least total cost.
We call this problem the minimum cost tree problem. It is often called the minimum spanning tree problem"spanning" being a term applying to any subgraph that includes every possible node of the original graph.
The minimum cost tree problem turns out to one that is both remarkably important in real life and also easy to compute. Below is an algorithm that starts with a graph G = (V,E) and computes a set of edges T that form a tree (V,T) that has minimum possible total cost:
 create a set T, initially equal to { }
 create a set F, initially equal to E
 if F = { } then go to step 7
 remove an edge {x,y} with
minimum cost from F
 if x and y are already connected by a path of edges in T, then discard edge {x,y}; otherwise add edge {x,y} to T
 go to step 3
 donethe minimum cost tree is (V,T)
Example
We will apply Kruskal's algorithm to the graph above. Recall that
set F initially includes every edge. Set T is initially empty. As we
add edges to T in the example below, we color those edges green.
This is our original graph. The numbers near the arcs indicate their weight. None of the arcs are highlighted. 

{A,D} and {C,E} are the shortest arcs, with length 5, and {A,D} has been arbitrarily chosen, so it is highlighted. 

However, {C,E} is now the shortest arc that does not form a cycle, with length 5, so it is highlighted as the second arc. 

The next arc, {D,F} with length 6, is highlighted using much the same method. 

The nextshortest arcs are {A,B} and {B,E}, both with length 7. {A,B} is chosen arbitrarily, and is highlighted. The arc {B,D} has been highlighted in red, because it would form a cycle (A,B,D,A) if it were chosen. 

The process continutes to highlight the nextsmallest arc,
{B,E} with length 7. Many more arcs are highlighted in red at this stage:


Finally, the process finishes with the arc {E,G} of length 9, and the minimum spanning tree is found. 
Google Rankings
Google's search engine relies heavily on network mathematics to rank websites and automatically calculate which ones are the "best" answer to a query.Centrality
The core network mathematical concept in Google rankings is the notion of centrality. This is an intuitive notion that is surprisingly hard to pin down mathematically. For example, which node is the most central in the following graph? Outdegree Centrality. Recall the difference between indegree and outdegree (see Degree). "Outdegree centrality" defines centrality as a measure of how many links point out of a node. A webpage that has many clickable links on the screen has high outdegree centrality. A webpage with very few clickable links has low outdegree centrality. In the above graph, node 3 has the highest outdegree centrality; it is the only node with 3 outgoing links. Google does not consider outdegree centrality.
 Indegree Centrality defines centrality as a measure of how many links point into a node. A webpage that is linked to by many other webpages has high indegree centrality. A webpage that is not linked to by many other webpages has low indegree centrality. Node 6 above has the highest indegree centrality; it is the only node with 4 incoming links. Unlike outdegree, indegree centrality does correspond to the popularity (or "goodness") of a website. Google uses something very much like indegree centrality.
 Eigenvector Centrality is
a more refined version of indegree centrality that is used by Google to
rank websites. The key refinement is that not every link counts equally
in computing eigenvector centralitylinks from more popular neighbors
count more than links from less popular neighbors. For example, notice
that nodes 2, 3, and 5 all have equal indegree centrality (which is 1).
However, node 5 is recommended by node 4, which is a very popular node.
Nodes 2 and 3 are not popular and only receive recommendations from
each other. So node 5 has significantly higher eigenvector centrality
than nodes 2 and 3.
Eigenvector is an advanced concept in linear algebra. Roughly speaking, an eigenvector is a critical point of a complex systema single point that creates a systemwide reverberation (like pushing a swing at just the right time provides high influence with low effort vs trying to push a swing out of synch with its natural rhythm provides low influence no matter how hard you try). Similarly, eigenvector centrality represents the extent to which a single node represents a critical point of influence or authority in a graph.
Although eigenvector centrality is complicated to compute, there are simple ways to calculate something similar. Below is an example of a simple iterative approach to calculating systemwide authority of a node in a graph.
Using the same 6node graph as above, we start by defining the InNeighborhood of a node x to be all nodes y that have directed edges into x. Then the InDegree of a node is simply the cardinality of its InNeighborhood.
Node  InNeighborhood  InDegree 
1  {4,6}  2 
2  {3}  1 
3  {2}  1 
4  {3,5,6}  3 
5  {4}  1 
6  {1,2,3,5}  4 
InDegree is a very local measure of node authority. We can measure a slightly more systemwide sense of node authority by defining InDegree^{2} as follows:
For any node x, InDegree^{2} of x is the sum of the InDegrees of all nodes in the InNeighborhood of x.
Node  InNeighborhood  InDegree  InDegree^{2} 
1  {4,6}  2  7 = 3+4 
2  {3}  1  1 = 1 
3  {2}  1  1 = 1 
4  {3,5,6}  3  6 = 1+1+4 
5  {4}  1  3 = 3 
6  {1,2,3,5}  4  5 = 2+1+1+1 
We can then repeat the above process to calculate InDegree^{3} based on InDegree^{2}, then InDegree^{4} based on InDegree^{3}, etc. Below is the same graph as before, followed by a table of five successive calculations of InDegree:
Node  InNeighborhood  InDegree^{1}  InDegree^{2}  InDegree^{3}  InDegree^{4}  InDegree^{5} 
1  {4,6}  2  7  11  21  38 
2  {3}  1  1  1  1  1 
3  {2}  1  1  1  1  1 
4  {3,5,6}  3  6  9  19  29 
5  {4}  1  3  6  9  19 
6  {1,2,3,5}  4  5  12  19  32 
Notice that the rankings of relative node authority change from column to column in the above table. Sometimes node 6 is most authoritative; sometimes node 1 is most authoritative. With more advanced techniques, we can prove that the rank order of relative authority will eventually stabilize, and furthermore we can determine how many times we need to repeat such a calculation to achieve a reliable result.