August 10, 2005
A concept of neighborhood in complex networks is addressed based on the criterion of the minimal number os steps to reach other vertices. This amounts to, starting from a given network $R_1$, generating a family of networks $R_\ell, \ell=2,3,...$ such that, the vertices that are $\ell$ steps apart in the original $R_1$, are only 1 step apart in $R_\ell$. The higher order networks are generated using Boolean operations among the adjacency matrices $M_\ell$ that represent $R_\ell$. The families originated by the well known linear and the Erd\"os-Renyi networks are found to be invariant, in the sense that the spectra of $M_\ell$ are the same, up to finite size effects. A further family originated from small world network is identified.
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December 29, 2006
A concept of higher order neighborhood in complex networks, introduced previously (PRE \textbf{73}, 046101, (2006)), is systematically explored to investigate larger scale structures in complex networks. The basic idea is to consider each higher order neighborhood as a network in itself, represented by a corresponding adjacency matrix. Usual network indices are then used to evaluate the properties of each neighborhood. Results for a large number of typical networks are presen...
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This review presents an account of the major works done on spectra of adjacency matrices drawn on networks and the basic understanding attained so far. We have divided the review under three sections: (a) extremal eigenvalues, (b) bulk part of the spectrum and (c) degenerate eigenvalues, based on the intrinsic properties of eigenvalues and the phenomena they capture. We have reviewed the works done for spectra of various popular model networks, such as the Erd\H{o}s-R\'enyi r...
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In this article, we propose a new type of square matrix associated with an undirected graph by trading off the naturally imbedded symmetry in them. The proposed matrix is defined using the neighbourhood sets of the vertices. It is called as neighbourhood matrix and it is denoted by $ \mathcal{NM}(G)$ as this proposed matrix also exhibits a bijection between the product of the two graph matrices, namely the adjacency matrix and the graph Laplacian. This matrix can also be obta...
November 12, 2011
The paper is a brief survey of some recent new results and progress of the Laplacian spectra of graphs and complex networks (in particular, random graph and the small world network). The main contents contain the spectral radius of the graph Laplacian for given a degree sequence, the Laplacian coefficients, the algebraic connectivity and the graph doubly stochastic matrix, and the spectra of random graphs and the small world networks. In addition, some questions are proposed.
January 27, 2021
Complex networks has been a hot topic of research over the past several years over crossing many disciplines, starting from mathematics and computer science and ending by the social and biological sciences. Random graphs were studied to observe the qualitative features they have in common in planetary scale data sets which helps us to project the insights proven to real world networks. In this paper, We survey the particular case of small-world phenomena and decentralized s...
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We study the properties of random graphs where for each vertex a {\it neighbourhood} has been previously defined. The probability of an edge joining two vertices depends on whether the vertices are neighbours or not, as happens in Small World Graphs (SWGs). But we consider the case where the average degree of each node is of order of the size of the graph (unlike SWGs, which are sparse). This allows us to calculate the mean distance and clustering, that are qualitatively simi...
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We study complex networks under random matrix theory (RMT) framework. Using nearest-neighbor and next-nearest-neighbor spacing distributions we analyze the eigenvalues of adjacency matrix of various model networks, namely, random, scale-free and small-world networks. These distributions follow Gaussian orthogonal ensemble statistic of RMT. To probe long-range correlations in the eigenvalues we study spectral rigidity via $\Delta_3$ statistic of RMT as well. It follows RMT pre...
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We propose a general approach to the description of spectra of complex networks. For the spectra of networks with uncorrelated vertices (and a local tree-like structure), exact equations are derived. These equations are generalized to the case of networks with correlations between neighboring vertices. The tail of the density of eigenvalues $\rho(\lambda)$ at large $|\lambda|$ is related to the behavior of the vertex degree distribution $P(k)$ at large $k$. In particular, as ...
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What is a complex network? How do we characterize complex networks? Which systems can be studied from a network approach? In this text, we motivate the use of complex networks to study and understand a broad panoply of systems, ranging from physics and biology to economy and sociology. Using basic tools from statistical physics, we will characterize the main types of networks found in nature. Moreover, the most recent trends in network research will be briefly discussed.
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Researchers have long observed that the "small-world" property, which combines the concepts of high transitivity or clustering with a low average path length, is ubiquitous for networks obtained from a variety of disciplines including social sciences, biology, neuroscience, and ecology. However, we find three shortcomings of the currently popular definition and detection methods rendering the concept less powerful. First, the classical definition combines high transitivity wi...