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The Laplacian matrix is an important topic in the theory of graph spectra.
Of fundamental importance in the theory of graph C ∗ -algebras is the fact that O 2 ⊗ O 2 ≅ O 2 (homeomorphically).
For more details in the theory of graph energy, see [3] by Li et al. The Laplacian matrix (L(G)) of a graph G is another important and well-studied matrix in graph theory, which is defined to be (L(G =D(G -A(G -A where (D(G)) is the diagonal matrix of vertex degrees of G.
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Other generalized Chebyshev polynomials (known as Shabat polynomials) have been introduced in [5] and they are studied in the theory of graphs on surfaces and curves over number fields.
The basic results also have various applications in the theories of graph decompositions, graph embeddings, and block designs.
To improve the GA optimization of the brace-frame systems, the theory of graphs is also utilized in the chapter.
The recent developments in the theory of complex graphs has made available many analytical tools for studying biological networks [5], [6].
By means of several transformations one establishes matrix formulae based on equations of radiative exchanges containing variables defined in a discrete analog form, and further on, by means of a multidisciplinary approach, the solutions are organized in algorithms that use the theory of networks, graphs and electric circuits.
In 1981, Palm proposed a mathematical interpretation of Hebbian theory in the framework of graph theory.
As well as having connections to the theory of random walks on graphs, the Laplacian eigenvalues can be used to define the expander graphs, an important class of graphs that have applications in coding theory.
The ROC graph was designed in the context of the theory of signal detectability [ 27, 28].
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