12.1 $\mathtt{AdjacencyMatrix}$: Representing a Graph by a Matrix

An adjacency matrix is a way of representing an $\mathtt{n}$ vertex graph $G=(V,E)$ by an $\ensuremath{\mathtt{n}}\times\ensuremath{\mathtt{n}}$ matrix, $\mathtt{a}$, whose entries are boolean values.

  int n;
  bool **a;

The matrix entry $\mathtt{a[i][j]}$ is defined as

$$\ensuremath{\mathtt{a[i][j]}}= \begin{cases} \ensuremath{\math... ...(i,j)}}\in E$} \\ \ensuremath{\mathtt{false}} & \text{otherwise} \end{cases}$$

The adjacency matrix for the graph in Figure 12.1 is shown in Figure 12.2.

In this representation, the operations $\mathtt{addEdge(i,j)}$, $\mathtt{removeEdge(i,j)}$, and $\mathtt{hasEdge(i,j)}$ just involve setting or reading the matrix entry $\mathtt{a[i][j]}$:

  void addEdge(int i, int j) {
    a[i][j] = true;
  }
  void removeEdge(int i, int j) {
    a[i][j] = false;
  }
  bool hasEdge(int i, int j) {
    return a[i][j];
  }

These operations clearly take constant time per operation.

Figure 12.2: A graph and its adjacency matrix.

	0	1	2	3	4	5	6	7	8	9	10	11
0	0	1	0	0	1	0	0	0	0	0	0	0
1	1	0	1	0	0	1	1	0	0	0	0	0
2	1	0	0	1	0	0	1	0	0	0	0	0
3	0	0	1	0	0	0	0	1	0	0	0	0
4	1	0	0	0	0	1	0	0	1	0	0	0
5	0	1	1	0	1	0	1	0	0	1	0	0
6	0	0	1	0	0	1	0	1	0	0	1	0
7	0	0	0	1	0	0	1	0	0	0	0	1
8	0	0	0	0	1	0	0	0	0	1	0	0
9	0	0	0	0	0	1	0	0	1	0	1	0
10	0	0	0	0	0	0	1	0	0	1	0	1
11	0	0	0	0	0	0	0	1	0	0	1	0

Where the adjacency matrix performs poorly is with the $\mathtt{outEdges(i)}$ and $\mathtt{inEdges(i)}$ operations. To implement these, we must scan all $\mathtt{n}$ entries in the corresponding row or column of $\mathtt{a}$ and gather up all the indices, $\mathtt{j}$, where $\mathtt{a[i][j]}$, respectively $\mathtt{a[j][i]}$, is true.

  void outEdges(int i, List &edges) {
    for (int j = 0; j < n; j++)
      if (a[i][j]) edges.add(j);
  }
  void inEdges(int i, List &edges) {
    for (int j = 0; j < n; j++)
      if (a[j][i]) edges.add(j);
  }

These operations clearly take $O(\ensuremath{\mathtt{n}})$ time per operation.

Another drawback of the adjacency matrix representation is that it is large. It stores an $\ensuremath{\mathtt{n}}\times \ensuremath{\mathtt{n}}$ boolean matrix, so it requires at least $\ensuremath{\mathtt{n}}^2$ bits of memory. The implementation here uses a matrix of $\mathtt{bool}$ values so it actually uses on the order of $\ensuremath{\mathtt{n}}^2$ bytes of memory. A more careful implementation, which packs $\mathtt{w}$ boolean values into each word of memory, could reduce this space usage to $O(\ensuremath{\mathtt{n}}^2/\ensuremath{\mathtt{w}})$ words of memory.

Theorem 12..1   The $\mathtt{AdjacencyMatrix}$ data structure implements the $\mathtt{Graph}$ interface. An $\mathtt{AdjacencyMatrix}$ supports the operations

• $\mathtt{addEdge(i,j)}$, $\mathtt{removeEdge(i,j)}$, and $\mathtt{hasEdge(i,j)}$ in constant time per operation; and
• $\mathtt{inEdges(i)}$, and $\mathtt{outEdges(i)}$ in $O(\ensuremath{\mathtt{n}})$ time per operation.

The space used by an $\mathtt{AdjacencyMatrix}$ is $O(\ensuremath{\mathtt{n}}^2)$.

Despite its high memory requirements and poor performance of the $\mathtt{inEdges(i)}$ and $\mathtt{outEdges(i)}$ operations, an $\mathtt{AdjacencyMatrix}$ can still be useful for some applications. In particular, when the graph $G$ is dense, i.e., it has close to $\ensuremath{\mathtt{n}}^2$ edges, then a memory usage of $\ensuremath{\mathtt{n}}^2$ may be acceptable.

The $\mathtt{AdjacencyMatrix}$ data structure is also commonly used because algebraic operations on the matrix $\mathtt{a}$ can be used to efficiently compute properties of the graph $G$. This is a topic for a course on algorithms, but we point out one such property here: If we treat the entries of $\mathtt{a}$ as integers (1 for $\mathtt{true}$ and 0 for $\mathtt{false}$) and multiply $\mathtt{a}$ by itself using matrix multiplication then we get the matrix $\ensuremath{\mathtt{a}}^2$. Recall, from the definition of matrix multiplication, that

$$\ensuremath{\mathtt{a^2[i][j]}} = \sum_{k=0}^{\ensuremath{\mathtt... ...1} \ensuremath{\mathtt{a[i][k]}}\cdot \ensuremath{\mathtt{a[k][j]}} \enspace .$$

Interpreting this sum in terms of the graph $G$, this formula counts the number of vertices, $\ensuremath{\mathtt{k}}$, such that $G$ contains both edges $\mathtt{(i,k)}$ and $\mathtt{(k,j)}$. That is, it counts the number of paths from $\ensuremath{\mathtt{i}}$ to $\ensuremath{\mathtt{j}}$ (through intermediate vertices, $\ensuremath{\mathtt{k}}$) whose length is exactly two. This observation is the foundation of an algorithm that computes the shortest paths between all pairs of vertices in $G$ using only $O(\log \ensuremath{\mathtt{n}})$ matrix multiplications.