Network Matrices


In this post we’re going to write about a special case of the \{0, \pm 1\} matrix, which is called network matrix. They play an important role in our study of totally unimodular matrices.

We’ll first define a network matrix and provide an example. We’ll later show some properties and then describe a polynomial-time algorithm to recognize network matrices.


Network matrices were first defined by William Tutte (1965) [1]. The definition is the following:

Let D = (V, A) be a directed graph and T = (V, A_0) be a directed tree on the vertex set V. Let M be the matrix A_0 \times A-matrix defined by a = (v, w) \in A and a' \in A_0.

M_{a',a} = \left\{ \begin{array}{ll}   +1 & \mbox{if the path } v\mbox{-}w \in T \mbox{ passes through}\\     & a \mbox{ in the same direction.}\\  -1 & \mbox{if the path } v\mbox{-}w \in T \mbox{ passes through}\\     & a \mbox{ in the opposite direction}.\\  0  & \mbox{if does not pass through } a \mbox{ at all.}  \end{array} \right.

Matrices with this structure are called network matrices.

Example. In Figure 1 we see a sample digraph G(V,A) and a given directed tree T on the vertex set V. The corresponding network matrix M represented by G and T is given on Table 1.


Figure 1. Directed graph G(V,A) and a directed tree T on V

For each arc (u,v) \in A, we have a column in M representing the path from u to v in T. The signs indicate the direction of the edges in the path.

Table 1. Network Matrix of G and T

Table 1. Network Matrix of G and T


Let M be a network matrix, represented by the digraph G and the directed tree T.

1) Multiplying a row of M by -1 is equivalent to inverting the direction of the corresponding edge in T.

2) Multiplying a column of M by -1 is the same as inverting the direction of the corresponding edge in G.

3) Deleting a row of M corresponding to edge (i, j) of T, is the same as shrinking vertex i and j into a single vertex.

4) Deleting a column of M is equivalent to removing the corresponding edge from G.

Combining properties (3) and (4), we have that:

5) A submatrix of a network matrix is a network matrix as well.

The following theorem is the most important property about Network Matrices, that we’ll explore in the forthcoming posts about Integer Programming Theory:

Theorem 1. Networks matrices are Totally Unimodular.

Recognizing Network Matrices

There is a polynomial-time algorithm for deciding whether a matrix M is a network matrix. Without loss of generality we restrict ourselves to \{0, \pm 1\} matrices, since all network matrices are of this type and we can easily find out whether a matrix has such property.

We now divide it in two cases. Case 1: for all columns of M, it has at most two non-zero entries; Case 2: the remaining types, that is, M has at least one column with three or more non-zero entries.

Case 1. Let’s describe the algorithm for the first case. Let M be a m \times n, \{0,\pm 1\} matrix with at most 2 non-zero entries.

If M has, for each column, at most one entry 1 and at most on entry -1, we can show it’s a network matrix by constructing a digraph G and a tree T such that their corresponding network matrix is M. We first build a direct star T with m vertices and with all edges pointing towards the center vertex v^*. We now build the digraph G with the same vertex set. For each column in M that has an entry +1 and -1 for rows (u,v^*) and (w,v^*) we add an edge (u,w). For columns having a single entry for row (u, v^*), we add the (u,v^*) to G if it’s +1 or (v^*,u) if it’s -1.

The problem is that even in the restrictive Case 1, we may have both entries with the same signal. From property (1) though, we can multiply some rows by -1 and try to reach the case above. The question is then: can we split the rows into sets R_1 and R_2, in such a way that if we multiply rows in R_2 by -1, we have at most one entry +1 and at most one entry -1 for all columns?

This question can be answered by a neat reduction to problem of deciding wether a graph is bipartite. Let G_R be a graph with vertex set corresponding to each row of M plus some artificial vertices. We add an edge (i, j) if the corresponding rows have the same signal for some column. We add edges (i, v_{ij}^*) and (j, v_{ij}^*) if the have different signs. We then try to split this graph into two partitions R_1 and R_2.

The idea is that if such a partitioning exists, vertex with different signs will be in the same partition because they share a common vertex and vertex with the same signs must be on different partitions. If we now multiply all rows in R_2 by -1, we’ll have our property.

Conversely, if no partition exists, it’s possible to show (see Example 1, from this post) that such matrix is not TU. Since by Theorem 1 all network matrices are TU, we conclude that this matrix is also not a network matrix. Summarizing we have that

Observation 1. The matrix M from the Case 1 is a network matrix if and only if its graph G_R define as above is bipartite.

Case 2. Now let’s concentrate on the more general case, where the matrix has at least one column with 3 or more non-zero entries.

For each row i of M, we define a graph G_i with vertex set \{1, \cdots, m\} \setminus \{i\}. There’s an edge between j and k if exists any column in M such that it has non-zero entries for j and k, but 0 for i. We have the following observation:

Observation 2. If M is a network matrix, there exists a row i for which G_i is disconnected.

The basic idea in understanding this observation is that since there is at least one column in M with three non-entries, there must be a path in T that connects two vertices in G with length at least 3. There is some edge i from the middle of this path that is not the first nor the last edge, henceforth denoted as j and k. If we remove this edge, we will split it into two components. It’s then easy to see that any path in the tree that has j and k needs to go through i. In turn, this means that there is no edge between the corresponding vertices in G_i.

From Observation 2, we can conclude that if a given matrix has G_i for all possible columns i, then M is not a network matrix.

So we can now suppose our candidate matrix has a disconnected G_1 (we can assume i = 1 without loss of generality. Let C_1, \cdots, C_p be the connected components of G_1.

We define

* W := as the set of column indexes for which the first row of M has non-zero entries;

* W_i := W \cap the set of column indexes for which the i-th row of M has non-zero entries;

* U_k := \bigcup \{W_i | i\in C_k\}

Now, we build a graph H with vertex set \{C_1, \cdots, C_p\}, with an edge (C_k, C_\ell) if the following conditions are met:

* \exists i \in C_k: U_\ell \not \subseteq W_i and U_\ell \cap W_i \neq \emptyset

* \exists j \in C_\ell: U_k \not \subseteq W_j and U_k \cap W_j \neq \emptyset

and let M_k be the submatrix formed by the rows of M corresponding to vertices in C_k.

We now can state the following Theorem:

Theorem 2. M is a Network Matrix if and only if: H is bipartite and M_k is a network matrix for k=1, \cdots, p.


Theorem 3. The algorithm above to detect whether a given matrix M is a network matrix has polynomial-time complexity.

1. Check if has only entries in \{0, \pm 1\}

2. Test if has at most two non-zero entries for each column (Case 1 or Case 2)

3. Case 1: Construct the graph G_R and check whether it is bipartite.

4. Case 2: For each column i, construct graph G_i and check if it is not connected. In this case, we build the graph H and build each of the submatrices M_k.

If we assume the matrix is m \times n, (1) and (2) can be performed in linear size of the matrix, O(mn). We can construct G_R in O(mn) as well and test for bipartiteness in linear time on the number of vertices plus the edges of G_R, which are both O(n + m), so (3) is bounded by O(mn) too.

For step (4), we need to generate the graph G_i. Its edges are bounded by mn and we can decide it’s connected in linear time of its size. Doing this for all n columns we a total complexity of O(mn^2).

For the recursive complexity, we can relax our complexity for Steps 1 to 4 to be O(n^tm^t) for a fixed constant t \ge 2. By induction, we assume our total complexity for a given level of the recursion is O(n^{t+1}m^t).

The matrix M_k has m_k rows and n columns, can be solved, by induction, in O(m_k^{t+1}n^t). Summing up all the complexities we have:

O(m^tn^t) + O(m_1^{t+1}n^t) + O(m_2^{t+1}n^t) + \cdots + O(m_p^{t+1}n^t) which is O(m^{t+1}n^t)

The base is when we do steps 1 to 4 without building any submatrices, which we saw it is O(m^tn^t).


The main points to be taken from this post is that network matrices are totally unimodular and that they can be recognized in polynomial-time.

We provided explanations about the two observations, but we left out the proofs of the Theorems 1 and 2, which are quite long and complicated, and thus out of the scope of the post. Nevertheless, they can be found on [1].


[1] Theory of Linear and Integer Programming – A. Schrijver (Chapter 19)


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