Transcript Mathematics of Networks (cont) - Computer Science & Engineering

Lecture 5:
Mathematics of Networks (Cont)
CS 790g: Complex Networks
Slides are modified from Networks: Theory and Application by Lada Adamic
Characterizing networks:
How far apart are things?
2
Network metrics: paths
 A path is any sequence of vertices such that every
consecutive pair of vertices in the sequence is
connected by an edge in the network.
 For directed: traversed in the correct direction for the edges.
 path can visit itself (vertex or edge) more than once
 Self-avoiding paths do not intersect themselves.
 Path length r is the number of edges on the path
 Called hops
3
Network metrics: paths

4
Network metrics: shortest paths

B
3
A
C
2
1
3
E 2
D
5
Structural metrics:
Average path length
1 ≤ L ≤ D ≤ N-1
6
Eulerian Path
 Euler’s Seven Bridges of Königsberg
 one of the first problems in graph theory
 Is there a route that crosses each bridge only once and returns to
the starting point?
Source: http://en.wikipedia.org/wiki/Seven_Bridges_of_Königsberg
Image 1 – GNU v1.2: Bogdan, Wikipedia; http://commons.wikimedia.org/wiki/Commons:GNU_Free_Documentation_License
Image 2 – GNU v1.2: Booyabazooka, Wikipedia; http://commons.wikimedia.org/wiki/Commons:GNU_Free_Documentation_License
Image 3 – GNU v1.2: Riojajar, Wikipedia; http://commons.wikimedia.org/wiki/Commons:GNU_Free_Documentation_License
Eulerian and Hamiltonian paths
Eulerian path: traverse each
edge exactly once
Hamiltonian path: visit
each vertex exactly once
 Hamiltonian path is self avoiding
If starting point and end point are the same:
only possible if no nodes have an odd degree as each path must visit and
leave each shore
If don’t need to return to starting point
can have 0 or 2 nodes with an odd degree
Characterizing networks:
Is everything connected?
9
Network metrics: components
 If there is a path from every vertex in a network to every
other, the network is connected
 otherwise, it is disconnected
 Component: A subset of vertices such that there exist at
least one path from each member of the subset to others
and there does not exist another vertex in the network
which is connected to any vertex in the subset
 Maximal subset
 A singeleton vertex that is not connected to any other
forms a size one component
 Every vertex belongs to exactly one component
10
components in directed networks
 Weakly connected components: every node can be reached from
every other node by following links in either direction
B
F
Weakly connected components
ABCDE
GHF
A
E
 Strongly connected components
G
C
H
D
 Each node within the component can be reached from every
other node in the component by following directed links
B
F
Strongly connected components
BCDE
A
GH
F
G
C
A
E
D
H
11
components in directed networks
 Every strongly connected component of more than one
vertex has at least one cycle
 Out-component: set of all vertices that are reachable
via directed paths starting at a specific vertex v
B
 Out-components of all members of a
F
G
C
A
strongly connected component are
identical
E
D
H
 In-component: set of all vertices from which there is a
direct path to a vertex v
 In-components of all members of a strongly connected
component are identical
12
network metrics: size of giant component
 if the largest component encompasses a significant fraction of the graph,
it is called the giant component
13
bowtie model of the web
 The Web is a directed graph:
 webpages link to other webpages
 The connected components tell us what set of pages can
be reached from any other just by surfing
 no ‘jumping’ around by typing in a URL or using a search engine
 Broder et al. 1999 – crawl of over 200 million pages and
1.5 billion links.
 SCC – 27.5%
 IN and OUT – 21.5%
 Tendrils and tubes – 21.5%
 Disconnected – 8%
14
Independent paths
 Edge independent paths: if they share no common edge
 Vertex independent paths: if they share no common
vertex except start and end vertices
 Vertex-independent => Edge-independent
 Also called disjoint paths
 These set of paths are not necessarily unique
 Connectivity of vertices: the maximal number of
independent paths between a pair of vertices
 Used to identify bottlenecks and resiliency to failures
15
Cut Sets and Maximum Flow
 A minimum cut set is the smallest cut set that will
disconnect a specified pair of vertices
 Need not to be unique
 Menger’s theorem: If there is no cut set of size less than
n between a pair of vertices, then there are at least n
independent paths between the same vertices.
 Implies that the size of min cut set is equal to maximum number
of independent paths (for both edge and vertex independence)
 Maximum Flow between a pair of vertices is the number
of edge independent paths times the edge capacity.
16
Graph Laplacian
17
Eigenvalues and eigenvectors
 Eigenvalues and eigenvectors have their origins in
physics, in particular in problems where motion is
involved, although their uses extend from solutions to
stress and strain problems to differential equations and
quantum mechanics.
 Eigenvectors are vectors that point in directions
where there is no rotation. Eigenvalues are the change
in length of the eigenvector from the original length.
 The basic equation in eigenvalue problems is:
Ax  x
Slides from Fred K. Duennebier
Eigenvalues and eigenvectors
Ax  x
(E.01)
 In words, this deceptively simple equation says that for
the square matrix A, there is a vector x such that the
product of Ax such that the result is a SCALAR, , that,
when multiplied by x, results in the same product. The
multiplication of vector x by a scalar constant is the
same as stretching or shrinking the coordinates by a
constant value.

Ax  x
The vector x is called an eigenvector and the scalar , is
called an eigenvalue.
Do all matrices have real eigenvalues?
No, they must be square and the determinant of A- I
must equal zero. This is easy to show:
Ax  x  0 xA  I  0
(E.02)
This can only be true if det(A- I )=|A- I |=0
(E.03)
Are eigenvectors unique?

No, if x is an eigenvector, then x is also an eigenvector
and   is an eigenvalue.
A(x)= Ax = x = ( x)
(E.04)
How do you calculate eigenvectors and eigenvalues?
Expand equation (E.03): det(A- I )=|A- I |=0 for a 2x2 matrix:
a11 a12  1 0 a11  
a12 
A  I  
 
 

a22   
a21 a22  0 1  a21
detA  I 
a11  
a12
a21
a22  
 a11   a22     a12a21  0
0  a11a22  a12a21   a11  a22  
2
(E.05)
For a 2-dimensional problem such as this, the equation above is a simple
quadratic equation with two solutions for . In fact, there is generally one
eigenvalue for each dimension, but some may be zero, and some complex.

The solution to E.05 is:
0  a11a22  a12a21  a11  a22   2
  a11  a22 
a11  a22 
(E.06)
2
4 a11a22  a12a21 
(E.07)
This “characteristic equation” does not involve x, and the resulting values of 
can be used to solve for x.
Consider the following example:
1 2
A  

2 4
Eqn. E.07 doesn’t work here because a11a22-a12a12=0, so we use E.06:

0  a11a22  a12a21  a11  a22   2
0  1 4  2  2  (1 4)  2
(1 4)  2
We see that one solution to this equation is =0, and dividing both sides of the
above equation by  yields =5.
Thus we have our two eigenvalues, and the eigenvectors for the first
eigenvalue, =0 are:
Ax  x,
A  Ix  0
1 2 0 x 1 2 x 1x  2y  0

     
   
  
2 4 0 y 2 4 y 2x  4y 0
These equations are multiples of x=-2y, so the smallest whole number values
that fit are x=2, y=-1

For the other eigenvalue, =5:
1 2 5 0 x 4 2  x 4x  2y 0

 
   
   
  
2 4 0 5 y 2 1 y  2x 1y  0
-4x + 2y = 0, and 2x  y  0, so, x  1, y  2
This example is rather special; A-1 does not exist, the two
rows of A- I are dependent and thus one of the
eigenvalues is zero. (Zero is a legitimate eigenvalue!)
EXAMPLE: A more common case is A =[1.05 .05 ; .05 1]
used in the strain exercise. Find the eigenvectors and
eigenvalues for this A, and then calculate [V,D]=eig[A].
The procedure is:
1) Compute the determinant of A- I
2) Find the roots of the polynomial given by | A- I|=0
3) Solve the system of equations (A- I)x=0
What good are such things?
Consider the matrix:
.8 .3
A  

.2 .7
What is A100 ?
We can get A100 by multiplying matrices many many times:


.70 .45 3 .65 .525 100 .600 .600
2
A  
 A  
 A  

.30 .55
.35 .475
.400 .400
Or we could find the eigenvalues of A and obtain A100 very quickly using
eigenvalues.

For now, I’ll just tell you that there are two eigenvectors for A:
.6
.8
x1    and Ax1  
.4
.7
1 
.8
x 2    and Ax 2  
1
.7
.3.6
  x1 (1 = 1)
.2.4
.31  .5 
    (2 = 0.5)
.21 .5
The eigenvectors are x1=[.6 ; .4] and x2=[1 ; -1], and the eigenvalues are 1=1
and 2=0.5.
Note that if we multiply x1 by A, we get x1. If we multiply x1 by A again, we STILL
get x1. Thus x1 doesn’t change as we mulitiply it by An.
What about x2? When we multiply A by x2, we get x2/2, and if we multiply x2
by A2, we get x2/4 . This number gets very small fast.
Note that when A is squared the eigenvectors stay the same, but the
eigenvalues are squared!
Back to our original problem we note that for A100, the eigenvectors will be
the same, the eigenvalues 1=1 and 2=(0.5)100, which is effectively zero.
Each eigenvector is multiplied by its eigenvalue whenever A is applied,