Matrix Representation of Graphs
📂Graph Theory Matrix Representation of Graphs Definition Let’s assume that a graph G ( V , E ) G(V,E) G ( V , E ) is given.
Degree Matrix Let’s denote the degree d ( v i ) d(v_{i}) d ( v i ) of each vertex v i ∈ V v_{i}\in V v i ∈ V simply as d i d_{i} d i . The following matrix is called the degree matrix of G G G and is denoted as D ( G ) D(G) D ( G ) or simply D D D .
D ( G ) = d i a g ( d 1 , … , d n ) = [ d 1 0 ⋯ 0 0 d 2 ⋯ 0 ⋮ ⋮ ⋱ ⋮ 0 0 ⋯ d n ]
D(G) = \mathrm{diag} (d_{1}, \dots, d_{n}) =
\begin{bmatrix}
d_{1} & 0 & \cdots & 0
\\ 0 & d_{2} & \cdots & 0
\\ \vdots & \vdots & \ddots & \vdots
\\ 0 & 0 & \cdots & d_{n}
\end{bmatrix}
D ( G ) = diag ( d 1 , … , d n ) = d 1 0 ⋮ 0 0 d 2 ⋮ 0 ⋯ ⋯ ⋱ ⋯ 0 0 ⋮ d n
Here, d i a g \mathrm{diag} diag is a diagonal matrix .
Adjacency Matrix The following square matrix is called the adjacency matrix of G G G and is denoted as A ( G ) A(G) A ( G ) or simply A A A .
A = [ a i j ] = { 1 , i j ∈ E ( G ) 0 , i j ∉ E ( G )
A = [a_{ij}] = \begin{cases} 1 &, ij \in E(G)
\\ 0 &,ij \notin E(G) \end{cases}
A = [ a ij ] = { 1 0 , ij ∈ E ( G ) , ij ∈ / E ( G )
The adjacency matrix has many discrete properties and maintains the form of a matrix, which allows for the application of various linear algebra theories. Especially, its eigenvalues are frequently mentioned in various models applying networks.
Incident Matrix The following matrix is called the incident matrix of G G G and is denoted as M ( G ) M(G) M ( G ) or simply M M M .
M = [ m i j ] = { 1 , vertex i is incident with edge j 0 , otherwise
M = [m_{ij}] = \begin{cases} 1 &, \text{vertex } i \text{ is incident with edge } j
\\ 0 &, \text{otherwise} \end{cases}
M = [ m ij ] = { 1 0 , vertex i is incident with edge j , otherwise
Directed Incident Matrix Let’s assume G G G is a directed graph . The following matrix is called the directed incident matrix of G G G and is denoted as N ( G ) N(G) N ( G ) or simply N N N .
N = [ n i j ] = { 1 , vertex i is tail of edge j − 1 , vertex i is head of edge j 0 , otherwise
N = [n_{ij}] = \begin{cases} 1 &, \text{vertex } i \text{ is tail of edge } j
\\ -1 &, \text{vertex } i \text{ is head of edge } j
\\ 0 &, \text{otherwise} \end{cases}
N = [ n ij ] = ⎩ ⎨ ⎧ 1 − 1 0 , vertex i is tail of edge j , vertex i is head of edge j , otherwise
Example
For the graph shown above, the following matrices are obtained.
D = v 1 v 2 v 3 v 4 v 5 v 6 v 7 v 8 v 1 3 0 0 0 0 0 0 0 v 2 0 2 0 0 0 0 0 0 v 3 0 0 2 0 0 0 0 0 v 4 0 0 0 6 0 0 0 0 v 5 0 0 0 0 2 0 0 0 v 6 0 0 0 0 0 2 0 0 v 7 0 0 0 0 0 0 3 0 v 8 0 0 0 0 0 0 0 0
D = \begin{matrix}
& v_{1} & v_{2} & v_{3} & v_{4} & v_{5} & v_{6} & v_{7} & v_{8}
\\ v_{1} & 3 & 0 & 0 & 0 & 0 & 0 & 0 & 0
\\ v_{2} & 0 & 2 & 0 & 0 & 0 & 0 & 0 & 0
\\ v_{3} & 0 & 0 & 2 & 0 & 0 & 0 & 0 & 0
\\ v_{4} & 0 & 0 & 0 & 6 & 0 & 0 & 0 & 0
\\ v_{5} & 0 & 0 & 0 & 0 & 2 & 0 & 0 & 0
\\ v_{6} & 0 & 0 & 0 & 0 & 0 & 2 & 0 & 0
\\ v_{7} & 0 & 0 & 0 & 0 & 0 & 0 & 3 & 0
\\ v_{8} & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0
\end{matrix}
D = v 1 v 2 v 3 v 4 v 5 v 6 v 7 v 8 v 1 3 0 0 0 0 0 0 0 v 2 0 2 0 0 0 0 0 0 v 3 0 0 2 0 0 0 0 0 v 4 0 0 0 6 0 0 0 0 v 5 0 0 0 0 2 0 0 0 v 6 0 0 0 0 0 2 0 0 v 7 0 0 0 0 0 0 3 0 v 8 0 0 0 0 0 0 0 0
A = v 1 v 2 v 3 v 4 v 5 v 6 v 7 v 8 v 1 0 1 1 1 0 0 0 0 v 2 1 0 0 1 0 0 0 0 v 3 1 0 0 1 0 0 0 0 v 4 1 1 1 0 1 1 1 0 v 5 0 0 0 1 0 0 1 0 v 6 0 0 0 1 0 0 1 0 v 7 0 0 0 1 1 1 0 0 v 8 0 0 0 0 0 0 0 0
A = \begin{matrix}
& v_{1} & v_{2} & v_{3} & v_{4} & v_{5} & v_{6} & v_{7} & v_{8}
\\ v_{1} & 0 & 1 & 1 & 1 & 0 & 0 & 0 & 0
\\ v_{2} & 1 & 0 & 0 & 1 & 0 & 0 & 0 & 0
\\ v_{3} & 1 & 0 & 0 & 1 & 0 & 0 & 0 & 0
\\ v_{4} & 1 & 1 & 1 & 0 & 1 & 1 & 1 & 0
\\ v_{5} & 0 & 0 & 0 & 1 & 0 & 0 & 1 & 0
\\ v_{6} & 0 & 0 & 0 & 1 & 0 & 0 & 1 & 0
\\ v_{7} & 0 & 0 & 0 & 1 & 1 & 1 & 0 & 0
\\ v_{8} & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0
\end{matrix}
A = v 1 v 2 v 3 v 4 v 5 v 6 v 7 v 8 v 1 0 1 1 1 0 0 0 0 v 2 1 0 0 1 0 0 0 0 v 3 1 0 0 1 0 0 0 0 v 4 1 1 1 0 1 1 1 0 v 5 0 0 0 1 0 0 1 0 v 6 0 0 0 1 0 0 1 0 v 7 0 0 0 1 1 1 0 0 v 8 0 0 0 0 0 0 0 0
M = e 1 e 2 e 3 e 4 e 5 e 6 e 7 e 8 e 9 e 10 v 1 1 1 1 0 0 0 0 0 0 0 v 2 1 0 0 1 0 0 0 0 0 0 v 3 0 1 0 0 1 0 0 0 0 0 v 4 0 0 1 1 1 1 1 1 0 0 v 5 0 0 0 0 0 1 0 0 1 0 v 6 0 0 0 0 0 0 1 0 0 1 v 7 0 0 0 0 0 0 0 1 1 1 v 8 0 0 0 0 0 0 0 0 0 0
M = \begin{matrix}
& e_{1} & e_{2} & e_{3} & e_{4} & e_{5} & e_{6} & e_{7} & e_{8} & e_{9} & e_{10}
\\ v_{1} & 1 & 1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0
\\ v_{2} & 1 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0
\\ v_{3} & 0 & 1 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0
\\ v_{4} & 0 & 0 & 1 & 1 & 1 & 1 & 1 & 1 & 0 & 0
\\ v_{5} & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 1 & 0
\\ v_{6} & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 1
\\ v_{7} & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 1 & 1
\\ v_{8} & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0
\end{matrix}
M = v 1 v 2 v 3 v 4 v 5 v 6 v 7 v 8 e 1 1 1 0 0 0 0 0 0 e 2 1 0 1 0 0 0 0 0 e 3 1 0 0 1 0 0 0 0 e 4 0 1 0 1 0 0 0 0 e 5 0 0 1 1 0 0 0 0 e 6 0 0 0 1 1 0 0 0 e 7 0 0 0 1 0 1 0 0 e 8 0 0 0 1 0 0 1 0 e 9 0 0 0 0 1 0 1 0 e 10 0 0 0 0 0 1 1 0
For the directed graph shown above, the directed incident matrix is as follows.
N = e 1 e 2 e 3 e 4 e 5 e 6 e 7 e 8 e 9 e 10 v 1 1 1 1 0 0 0 0 0 0 0 v 2 − 1 0 0 1 0 0 0 0 0 0 v 3 0 − 1 0 0 1 0 0 0 0 0 v 4 0 0 − 1 − 1 − 1 1 1 1 0 0 v 5 0 0 0 0 0 − 1 0 0 1 0 v 6 0 0 0 0 0 0 − 1 0 0 1 v 7 0 0 0 0 0 0 0 − 1 − 1 − 1 v 8 0 0 0 0 0 0 0 0 0 0
N = \begin{matrix}
& e_{1} & e_{2} & e_{3} & e_{4} & e_{5} & e_{6} & e_{7} & e_{8} & e_{9} & e_{10}
\\ v_{1} & 1 & 1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0
\\ v_{2} & -1 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0
\\ v_{3} & 0 & -1 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0
\\ v_{4} & 0 & 0 & -1 & -1 & -1 & 1 & 1 & 1 & 0 & 0
\\ v_{5} & 0 & 0 & 0 & 0 & 0 & -1 & 0 & 0 & 1 & 0
\\ v_{6} & 0 & 0 & 0 & 0 & 0 & 0 & -1 & 0 & 0 & 1
\\ v_{7} & 0 & 0 & 0 & 0 & 0 & 0 & 0 & -1 & -1 & -1
\\ v_{8} & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0
\end{matrix}
N = v 1 v 2 v 3 v 4 v 5 v 6 v 7 v 8 e 1 1 − 1 0 0 0 0 0 0 e 2 1 0 − 1 0 0 0 0 0 e 3 1 0 0 − 1 0 0 0 0 e 4 0 1 0 − 1 0 0 0 0 e 5 0 0 1 − 1 0 0 0 0 e 6 0 0 0 1 − 1 0 0 0 e 7 0 0 0 1 0 − 1 0 0 e 8 0 0 0 1 0 0 − 1 0 e 9 0 0 0 0 1 0 − 1 0 e 10 0 0 0 0 0 1 − 1 0