The spectral theory of higher-order symmetric tensors is an important tool for revealing some important properties of a hypergraph via its adjacency tensor, Laplacian tensor, and signless Laplacian tensor. Owing to the sparsity of these tensors, we propose an efficient approach to calculate products of these tensors and any vectors. By using the state-of-the-art L-BFGS approach, we develop a fi...

We consider the size and structure of the automorphism groups of a variety of empirical ‘realworld’ networks and find that, in contrast to classical random graph models, many real-world networks – including a variety of biological networks and technological networks such as the internet – are richly symmetric. We then discuss how knowledge of the structure of the automorphism group of a network...

An eigenvalue of a graph G is called main eigenvalue if it has an eigenvector the sum of whose entries is not equal to zero. Hoffman [A.J. Hoffman, On the polynomial of a graph, Amer. Math. Monthly 70 (1963) 30–36] proved that G is a connected k-regular graph if and only if n ∏t i=2(A− λiI ) = ∏t i=2(k − λi) · J , where I is the unit matrix and J the all-one matrix and λ1 = k, λ2, . . . , λt ar...

The parity-check matrix of a linear code is used to define a bipartite code constraint (Tanner) graph in which bit nodes are connected to parity check nodes. The connectivity properties of this graph are analyzed using both local connectivity and the eigenvalues of the associated adjacency matrix. A simple lower bound on minimum distance of the code is expressed in terms of the two largest eige...

The (n, k)-arrangement graph A(n, k) is a graph with all the k-permutations of an n-element set as vertices where two k-permutations are adjacent if they agree in exactly k − 1 positions. We introduce a cyclic decomposition for k-permutations and show that this gives rise to a very fine equitable partition of A(n, k). This equitable partition can be employed to compute the complete set of eigen...

In this paper, we represent protein structure by using graph. A protein structure database will become a graph database. Each graph is represented by a spectral vector. We use Jacobi rotation algorithm to calculate the eigenvalues of the normalized Laplacian representation of adjacency matrix of graph. To measure the similarity between two graphs, we calculate the Euclidean distance between two...

We study complex networks under random matrix theory (RMT) framework. Using nearest-neighbor and next-nearest-neighbor spacing distributions we analyze the eigenvalues of the adjacency matrix of various model networks, namely, random, scale-free, and small-world networks. These distributions follow the Gaussian orthogonal ensemble statistic of RMT. To probe long-range correlations in the eigenv...

Let G be a finite, undirected, and simple graph. If {v1, · · · , vn} is the set of vertices of G, then the adjacency matrix A(G) = [aij ] is an n-by-n matrix where aij = 1 if vi and vj are adjacent and aij = 0 otherwise. The energy of a graph, E(G), is defined as the sum of the absolute values of eigenvalues of A(G). The concept of energy originates in chemistry and was first defined by I. Gutm...

We consider the ensemble of adjacency matrices of Erdős-Rényi random graphs, i.e. graphs on N vertices where every edge is chosen independently and with probability p ≡ p(N). We rescale the matrix so that its bulk eigenvalues are of order one. We prove that, as long as pN →∞ (with a speed at least logarithmic in N), the density of eigenvalues of the Erdős-Rényi ensemble is given by the Wigner s...

Networks are continuously growing in complexity, which creates challenges for determining their most important characteristics. While analytical bounds are often too conservative, the computational effort of algorithmic approaches does not scale well with network size. This work uses Cartesian Genetic Programming for symbolic regression to evolve mathematical equations that relate network prope...