Who’s who in a
network?
The role a given vertice plays in the cohesiveness of the
network (its ‘importance’ or ‘centrality’ )can be assessed by a number of
social network metrics.
Degree Centrality
is simply the number of connections (or edges) a vertex has to other vertices.
Its clear from counting that #1’s degree centrality is 3, while vertex #4 has a
measure of degree centrality equal to 1.
In terms of matrix algebra, if we can obtain this result as
a vector (one entry per vertice) as follows:
x= (1,1,1,1) degree = A’x
Eigenvector Centrality
is a measure that reflects the fact that not all connections are equal, and in
fact, connections to people that are more influential are more important
(Newman, 2012). Mathematically, the
measure of eigenvector centrality is derived from the weights that consist of
the values from the leading eigenvector
(the eigenvector associated with the largest eigenvalue) of the
adjacency matrix depicting the network.
Recall, the value λ
is an eigenvalue (or vector of eigenvalues) of the matrix A, and x is an
eigenvector of A if the following
equation holds:
λ x = A x
So, for the network depicted above, the centrality measures
for each vertex = 1,2,3,4 can be derived from the components (x1,x2,x3,x4)
that comprise the eigenvector x, and
what we are interested in for this metric is the leading eigenvector
(associated with the largest eigenvalue of A).
Eigenvectors and eigenvalues can be derived by solving:
(A-λ I)x = 0
This metric can be found using canned functions in SAS and
R. In SAS IML, the eigen function will allow you to obtain the leading
eigenvector x =( 0.6116285,0.5227207,0.5227207,0.2818452).
A Path Centric
Approach to Centrality
One way of looking at centrality in terms of connectivity is
counting the number of paths emanating from a vertice to other vertices in the
network. For example, degree represents
the number of paths of length 1 to other vertices in the network. To look at this further, lets define the
matrix B as:
B = A + I, where
I is the identity matrix of A. The
elements of B represent the number of
paths of length 1 from a vertice ‘i’ to another vertice ‘j’. The row sums of B
tell us the total number of paths of length one emanating for a given vertice
(counting each vertice itself as 1). We can store this sum for each vertice in
a vector ‘d’ obtained by
multiplyting B’x.
This gives us B’x
= d1 = (4,3,3,2)
Now let’s define the matrix power of such that B2 = B*B.
Each entry in B2 represents the number of paths
of length 2 from vertice i to vertice j.
The sum d2 = B2 *x = d2 = (12,10,10,6)
is the total number of paths of length 2 originating from each corresponding
vertice.
So Bk is
a matrix with elements indicating the number of paths of length k between
vertices i and j. The product Bk*x
gives the sum of all paths of length k emanating from a given vertice. What we
find is that as we consider longer and longer paths (as k becomes large) the
normalized vector:
v = Bk*x /
|| Bk*x|| will approach
the leading eigenvector of B (which
consequently is also the leading eigenvector of A)
v1 = B1*x / || Bk*x|| = (0.6488857,0.4866643,0.4866643,0.3244428)
v5 = B5*x
/ || Bk*x|| = (0.6121647,0.521951, 0.521951,0.2835289)
v10 = B10*x
/ || Bk*x|| =( 0.6116348,0.5227115,0.5227115,0.2818657)
Recall the leading eigenvector of A:
x = ( 0.6116285,0.5227207,0.5227207,0.2818452)
So, what we have is a notion of eigenvector centrality as a
metric that captures the number of paths emanating from a given vertice. A
vertice that is connected to other vertices that also have lots of connections
will have a greater number of k-length paths originating from it. This is
information is reflected by eigenvector centrality. It turns out that the
iterative calculation of v above is essentially the power iteration algorithm for calculating
the leading eigenvector of a matrix B.
The following code from SAS IML and output provides an
illustration of the preceding discussion. In addition I have provided the function
written by Rick Wicklin that implements the power iteration algorithm used to
obtain the leading eigenvector and eigenvalue of a given matrix A.
REFERENCES:
Justification and Application of Eigenvector Centrality by
Leo Spizzirri https://www.math.washington.edu/~morrow/336_11/papers/leo.pdf
The power method: compute only the largest eigenvalue of
a matrix. The Do Loop. By Rick Wicklin.
SAS IML MATRIX PROGRAMMING:
proc iml;
*specify adjacency matrix A;
A = {0 1 1 1,
1 0 1 0,
1 1 0 0,
1 0 0 0};
x = {1,1,1,1};
* Degree centraltiy;
d1 = A*x;
print(A);
print(x);
print(d1);
call eigen(u,v,A);
print v;
B = A + I(4);
print B;
d1 = B*x;
print d1;
B2 = B**2;
print B2;
* Degree Centrality including
stopovers;
d1 = B*x;
print(d1);
d2 = (B**2)*x;
print(d2);
d3 = (B**3)*x;
print(d3);
* as k--> infinity, the
normalized vector v approaches the leading eigenvector of A and/or B;
v1 = d1/sqrt(d1[##]);
print v1;
d5 = (B**5)*x;
print(d5);
v5 = d5/sqrt(d5[##]);
print v5;
d10 = (B**10)*x;
print(d5);
v10 = d10/sqrt(d10[##]);
print v10;
run; quit;
* Rick Wicklin's Power Method
Function;
proc iml;
start PowerMethod(v,
A, maxIters);
/* specify relative tolerance used for convergence */
tolerance = 1e-6;
v = v / sqrt( v[##] ); /* normalize */
iteration = 0; lambdaOld = 0;
do while ( iteration <= maxIters);
z = A*v; /* transform */
v = z / sqrt( z[##] ); /* normalize */
lambda = v` * z;
iteration = iteration + 1;
if abs((lambda - lambdaOld)/lambda) < tolerance then
return ( lambda );
lambdaOld = lambda;
end;
return ( . ); /* no convergence */
finish;
* try our data;
A = {0 1 1 1,
1 0 1 0,
1 1 0 0,
1 0 0 0};
v = {1,1,1,1};
lambda = PowerMethod(v, A, 40 );
print lambda;
print v;
run;quit;
