By C. Pozrikidis

An advent to Grids, Graphs, and Networks goals to supply a concise creation to graphs and networks at a degree that's available to scientists, engineers, and scholars. In a pragmatic procedure, the booklet provides in simple terms the required theoretical recommendations from arithmetic and considers various actual and conceptual configurations as prototypes or examples. the topic is well timed, because the functionality of networks is famous as a big subject within the learn of complicated structures with purposes in strength, fabric, and knowledge grid delivery (epitomized via the internet). The e-book is written from the sensible viewpoint of an engineer with a few history in numerical computation and utilized arithmetic, and the textual content is observed by means of various schematic illustrations all through.

In the publication, Constantine Pozrikidis offers an unique synthesis of thoughts and phrases from 3 targeted fields-mathematics, physics, and engineering-and a proper program of strong conceptual apparatuses, like lattice Green's functionality, to components the place they've got not often been used. it's novel in that its grids, graphs, and networks are attached utilizing ideas from partial differential equations. This unique fabric has profound implications within the learn of networks, and may function a source to readers starting from undergraduates to skilled scientists.

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Extra info for An Introduction to Grids, Graphs, and Networks

Sample text

0 –1 1 .. 0 0 –1 .. ··· ··· ··· .. 0 0 0 .. 0 0 0 .. 5. 0 0 0 .. 14) L = R · RT . In fact, this factorization is valid for arbitrary node and link labeling and for general higher-dimensional graphs. 1 Node and link labeling Derive the connectivity lists and the oriented incidence matrix for an arbitrary node and link labeling scheme of your choice. 1 is a closed or periodic one-dimensional graph consisting of N unique nodes connected by L = N links. The N × N periodic graph Laplacian is ⎡ ⎤ 2 –1 0 ··· 0 0 –1 ⎢ –1 2 –1 ··· 0 0 0 ⎥ ⎢ ⎥ ⎢ ⎥ –1 1 ··· 0 0 0 ⎥ ⎢ 0 ⎢ ..

8) μn = 2 cos n–1 2π N for n = 1, . . , N. 7) are zero when s is zero or an odd integer and nonzero when s is an even integer. 9) n2 = 26, n4 = 78, n6 = 260, n8 = 910, n10 = 3276. 10) R = ⎢ ⎢ ⎢ ⎢ ⎢ ⎣ –1 1 0 .. 0 –1 1 .. 0 0 –1 .. ··· ··· ··· .. 0 0 0 .. 0 0 0 .. 0 0 0 0 0 0 0 0 0 ··· ··· ··· –1 1 0 0 –1 1 1 0 0 .. ⎤ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥. 11) L = D – A = R · RT , where D = 2 I is a diagonal matrix hosting the degree of the N nodes. 12) ψi = cp exp – i pk(i – 1) p = –M for i = 1, . . , N, where k ≡ 2π /N is the wave number of the longest wave, i is the imaginary unit, and cp are complex Fourier coefficients.

8), roughly stating that an equal number of eigenvector components with positive and negative sign appear. , [11]). 1. Positive components of an eigenvector are marked as filled circles, negative components are marked as dots, and zero components are unmarked. The network shown consists of N = 172 = 289 nodes connected by L = 544 links. The degrees of the 4 corner nodes is 2, the degrees of the 60 edge nodes is 3, and the degrees of the 225 interior nodes is 4. Exact expressions for the eigenvalues and eigenvectors of the Laplacian of the square network are available, as discussed in Chapter 3.