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[ "article:topic", "authorname:rhanneman", "showtoc:no" ]
Mathematics LibreTexts

5.4: Doing mathematical operations on matrices

  • Page ID
    7675
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    Representing the ties among actors as matrices can help us to see patterns by performing simple manipulations like summing row vectors or partitioning the matrix into blocks. Social network analysts use a number of other mathematical operations that can be performed on matrices for a variety of purposes (matrix addition and subtraction, transposes, inverses, matrix multiplication, and some other more exotic stuff like determinants and eigenvalues). Without trying to teach you matrix algebra, it is useful to know at least a little bit about some of these mathematical operations, and what they are used for in social network analysis.

    UCINET has built-in functions for doing most matrix algebra functions.  Look under the Tools>Matrix Algebra menu.  If you do know some matrix algebra, you will find that this tool lets you do almost anything to matrix data that you may desire.  But, you do need to know what you are doing.  The help screen for this command shows how to identify the matrix or matrices that are to be manipulated, and the algorithms that can be applied.

    Transposing a matrix

    This simply means to exchange the rows and columns so that i becomes j, and vice versa. If we take the transpose of a directed adjacency matrix and examine its row vectors (you should know all this jargon by now!), we are looking at the sources of ties directed at an actor. The degree of similarity between an adjacency matrix and the transpose of that matrix is one way of summarizing the degree of symmetry in the pattern of relations among actors. That is, the correlation between an adjacency matrix and the transpose of that matrix is a measure of the degree of reciprocity of ties (think about that assertion a bit). Reciprocity of ties can be a very important property of a social structure because it relates to both the balance and to the degree and form of hierarchy in a network.  This command is also available as Data>Transpose.

    Taking the inverse of a matrix

    This is a mathematical operation that finds a matrix which, when multiplied by the original matrix, yields a new matrix with ones in the main diagonal and zeros elsewhere (which is called an identity matrix). Without going any further into this, you can think of the inverse of a matrix as being sort of the "opposite of" the original matrix. Matrix inverses are used mostly in calculating other things in social network analysis. They are sometimes interesting to study in themselves, however. It is sort of like looking at black lettering on white paper versus white lettering on black paper: sometimes you see different things.  Inverses are calculated with Tools>Matrix Algebra.

    Matrix addition and matrix subtraction

    These are the easiest of matrix mathematical operations. One simply adds together or subtracts each corresponding i,j element of the two (or more) matrices. Of course, the matrices that this is being done to have to have the same numbers of I and j elements (this is called "conformable" to addition and subtraction) - and, the values of i and j have to be in the same order in each matrix. 

    Matrix addition and subtraction are most often used in network analysis when we are trying to simplify or reduce the complexity of multiplex (multiple relations recorded as separate matrices or slices) data to simpler forms. If I had a symmetric matrix that represented the tie "exchanges money" and another that represented the relation "exchanges goods" I could add the two matrices to indicate the intensity of the exchange relationship. Pairs with a score of zero would have no relationship, those with a "1" would be involved in either barter or commodity exchange, and those with a "2" would have both barter and commodity exchange relations. If I subtracted the "goods" exchange matrix from the "money exchange" matrix, a score of -1 would indicate pairs with a barter relationship; a score of zero would indicate either no relationship or a barter and commodity tie; a score of +1 would indicate pairs with only a commodified exchange relationship. For different research questions, either or both approaches might be useful.  Tools>Matrix Algebra are one way of doing these sorts of data transformations.  

    Matrix multiplication and Boolean matrix multiplication

    Matrix multiplication is a somewhat unusual operation, but can be very useful for the network analyst. You will have to be a bit patient here. First we need to show you how to do matrix multiplication and a few important results (like what happens when you multiply an adjacency matrix times itself, or raise it to a power). Then, we will try to explain why this is useful.

    To multiply two matrices, they must be "conformable" to multiplication. This means that the number of rows in the first matrix must equal the number of columns in the second. Usually network analysis uses adjacency matrices, which are square, and hence, conformable for multiplication.  Multiplying a matrix by itself (i.e. raising it to a power) and multiplying a square matrix by its transpose are obviously "conformable."  Unlike regular multiplication of individual numbers  X*Y is not the same thing as Y*X in matrix multiplication -- the order matters!

    To multiply two matrices, begin in the upper left hand corner of the first matrix, and multiply every cell in the first row of the first matrix by the values in each cell of the first column of the second matrix, and sum the results. Proceed through each cell in each row in the first matrix, multiplying by the column in the second. To perform a Boolean matrix multiplication, proceed in the same fashion, but enter a zero in the cell if the multiplication product is zero, and one if it is not zero.  An example helps.  Suppose we wanted to multiply the two matrices in figure 5.10.

    0 1
    2 3
    4 5

    times

    6 7 8
    9 10 11

    Figure 5.10. Two matrices to be multiplied.

    The result is shown in Figure 5.11.

    (0*6)+(1*9) (0*7)+(1*10) (0*8)+(1*11)
    (2*6)+(3*9) (2*7)+(3*10) (2*8)+(3*11)
    (4*6)+(5*9) (4*7)+(5*10) (4*8)+(5*11)

    Figure 5.11. Result of matrix multiplication.

    The mathematical operation in itself doesn't interest us here (any number of programs can perform matrix multiplication). But, the operation is useful when applied to an adjacency matrix. Consider our four friends again, in figure 5.12.

    Hanneman Screenshot 5-2.png

    Figure 5.12. Directed graph of friendship relations among Bob, Carol, Ted, and Alice

    The adjacency matrix for the four actors B, C, T, and A (in that order) is shown as Figure 5.13.

    ---

    1

    1

    0

    0

    ---

    1

    0

    1

    1

    ---

    1

    0

    0

    1

    ---

    Figure 5.13. Adjacency matrix for graph in Figure 5.12.

    Another way of thinking about this matrix is to notice that it tells us whether there is a path from each actor to each actor. A one represents the presence of a path, a zero represents the lack of a path. The adjacency matrix is exactly what its name suggests -- it tells us which actors are adjacent, or have a direct path from one to the other.

    Now suppose that we multiply this adjacency matrix times itself (i.e. raise the matrix to the 2nd power, or square it).  We will treat "self-ties" as zeros, which, effectively, ignores them.  The calculation of the matrix squared is shown as Figure 5.14.

    (0*0)+(1*0)+(1*1)+(0*0) (0*1)+(1*0)+(1*1)+(0*0) (0*1)+(1*1)+(1*0)+(0*1) (0*0)+(1*0)+(1*1)+(0*0)
    (0*0)+(0*0)+(1*1)+(0*0) (0*1)+(0*0)+(1*1)+(0*0) (0*1)+(0*1)+(1*0)+(0*1) (0*0)+(0*0)+(1*1)+(0*0)
    (1*0)+(1*0)+(0*1)+(1*0) (1*1)+(1*0)+(0*1)+(1*0) (1*1)+(1*1)+(0*0)+(1*1) (1*0)+(1*0)+(0*1)+(1*0)
    (0*0)+(0*0)+(1*1)+(0*0) (0*1)+(0*0)+(1*1)+(0*0) (0*1)+(0*1)+(1*0)+(0*1) (0*0)+(0*0)+(1*1)+(0*0)


    equals:

    1

    1

    1

    1

    1

    1

    0

    1

    0

    1

    3

    0

    1

    1

    0

    1

    Figure 5.14. Squaring matrix 5.13.

    This matrix (i.e. the adjacency matrix squared) counts the number of pathways between two nodes that are of length two. Stop for a minute and verify this assertion (go back to the graph and find the paths).  For example, note that actor "B" is connected to each of the other actors by a pathway of length two; and that there is no more than one such pathway to any other actor. Actor T is connected to himself by pathways of length two, three times. This is because actor T has reciprocal ties with each of the other three actors. There is no pathway of length two from T to B (although there is a pathway of length one).

    So, the adjacency matrix tells us how many paths of length one are there from each actor to each other actor. The adjacency matrix squared tells us how many pathways of length two are there from each actor to each other actor. It is true (but we won't show it to you) that the adjacency matrix cubed counts the number of pathways of length three from each actor to each other actor. And so on...

    If we calculated the Boolean product, rather than the simple matrix product, the adjacency matrix squared would tell us whether there was a path of length two between two actors (not how many such paths there were). If we took the Boolean squared matrix and multiplied it by the adjacency matrix using Boolean multiplication, the result would tell us which actors were connected by one or more pathways of length three. And so on...

    Now, finally: why should you care?

    Some of the most fundamental properties of a social network have to do with how connected the actors are to one another. Networks that have few or weak connections, or where some actors are connected only by pathways of great length may display low solidarity, a tendency to fall apart, slow response to stimuli, and the like. Networks that have more and stronger connections with shorter paths among actors may be more robust and more able to respond quickly and effectively. Measuring the number and lengths of pathways among the actors in a network allow us to index these important tendencies of whole networks.

    Individual actor's positions in networks are also usefully described by the numbers and lengths of pathways that they have to other actors. Actors who have many pathways to other actors may be more influential with regard to them. Actors who have short pathways to more other actors may me more influential or central figures. So, the number and lengths of pathways in a network are very important to understanding both individual's constraints and opportunities, and for understanding the behavior and potentials of the network as a whole.

    There are many measures of individual position and overall network structure that are based on whether there are pathways of given lengths between actors, the length of the shortest pathway between two actors, and the numbers of pathways between actors. Indeed, most of the basic measures of networks, measures of centrality and power, and measures of network groupings and substructures are based on looking at the numbers and lengths of pathways among actors.

    For most analyses, you won't have to manipulate matrices -- UCINET and other programs have already built algorithms that have the compute do these operations.  Most of the computational work in network analysis is done with matrix mathematics though, so in order to understand what is going on, it's useful to understand the basics.