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The Laplacian matrix of the graph is defined as L=D−A.
The adjacency matrix of the graph is defined as A i,j =w ij.
We recall that the Laplacian matrix of the graph G is L ( G ) = D ( G ) − A ( G ), where D ( G ) is the diagonal matrix of vertex degrees and A ( G ) is the ( 0, 1 ) -adjacency matrix of the graph G.
We can get the Laplacian matrix of the graph G by L_{text{mtx}}=D_{text{mtx}}-A_{text{mtx}}.
The degree matrix of the graph is a diagonal matrix defined as D=diag{d 1,d 2,…,d N }.
In this study, node reduction methods were used to reduce the size of the matrix of the graph.
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We characterise quasidiagonality of the C⁎-algebra of a cofinal k-graph in terms of an algebraic condition involving the coordinate matrices of the graph.
As shown in Fig. 2a and in Fig. 2b the matrices of the graph in Fig. 1 created where in Fig. 2a the matrices represent 0's and 1's where 0's represents no edges between certain nodes and 1's represent a weighted edge available between these nodes.
Let N be the number of nodes (in our case, genes) in the directed graph, and A be the adjacency matrix representation of the graph, a 0-1 matrif (if node i links to j, then A ij =1).
The information flow model first constructs a n× n transition matrix P by normalizing the adjacent matrix A of the graph G, where A ij = w((g i, g j )) and P ij =α A ij /∑ k A ik.α∈ 0, 1) is called 'damping factor'.
Such findings were also highlighted by the scatter graph and from the matrix of the scatter graph.
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