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Complex networks
This chapter contains a brief introduction to complex networks, and in particular to small world and scale free networks. We show how to apply the replica method developed to analyse random matrices in statistical physics to calculate the spectral densities of the adjacency and Laplacian matrices of a scale free network. We use the effective medium approximation to treat networks with finite mean degree and discuss the local properties of random matrices associated with complex networks
Pattern vectors from algebraic graph theory
Graphstructures have proven computationally cumbersome for pattern analysis. The reason for this is that, before graphs can be converted to pattern vectors, correspondences must be established between the nodes of structures which are potentially of different size. To overcome this problem, in this paper, we turn to the spectral decomposition of the Laplacian matrix. We show how the elements of the spectral matrix for the Laplacian can be used to construct symmetric polynomials that are permutation invariants. The coefficients of these polynomials can be used as graph features which can be encoded in a vectorial manner. We extend this representation to graphs in which there are unary attributes on the nodes and binary attributes on the edges by using the spectral decomposition of a Hermitian property matrix that can be viewed as a complex analogue of the Laplacian. To embed the graphs in a pattern space, we explore whether the vectors of invariants can be embedded in a low- dimensional space using a number of alternative strategies, including principal components analysis ( PCA), multidimensional scaling ( MDS), and locality preserving projection ( LPP). Experimentally, we demonstrate that the embeddings result in well- defined graph clusters. Our experiments with the spectral representation involve both synthetic and real- world data. The experiments with synthetic data demonstrate that the distances between spectral feature vectors can be used to discriminate between graphs on the basis of their structure. The real- world experiments show that the method can be used to locate clusters of graphs
A polynomial eigenvalue approach for multiplex networks
We explore the block nature of the matrix representation of multiplex
networks, introducing a new formalism to deal with its spectral properties as a
function of the inter-layer coupling parameter. This approach allows us to
derive interesting results based on an interpretation of the traditional
eigenvalue problem. More specifically, we reduce the dimensionality of our
matrices but increase the power of the characteristic polynomial, i.e, a
polynomial eigenvalue problem. Such an approach may sound counterintuitive at
first glance, but it allows us to relate the quadratic problem for a 2-Layer
multiplex system with the spectra of the aggregated network and to derive
bounds for the spectra, among many other interesting analytical insights.
Furthermore, it also permits to directly obtain analytical and numerical
insights on the eigenvalue behavior as a function of the coupling between
layers. Our study includes the supra-adjacency, supra-Laplacian, and the
probability transition matrices, which enable us to put our results under the
perspective of structural phases in multiplex networks. We believe that this
formalism and the results reported will make it possible to derive new results
for multiplex networks in the future.Comment: 15 pages including figures. Submitted for publicatio
Eigenvalue Outliers of non-Hermitian Random Matrices with a Local Tree Structure
Spectra of sparse non-Hermitian random matrices determine the dynamics of
complex processes on graphs. Eigenvalue outliers in the spectrum are of
particular interest, since they determine the stationary state and the
stability of dynamical processes. We present a general and exact theory for the
eigenvalue outliers of random matrices with a local tree structure. For
adjacency and Laplacian matrices of oriented random graphs, we derive
analytical expressions for the eigenvalue outliers, the first moments of the
distribution of eigenvector elements associated with an outlier, the support of
the spectral density, and the spectral gap. We show that these spectral
observables obey universal expressions, which hold for a broad class of
oriented random matrices.Comment: 25 pages, 4 figure
Estimation of Laplacian spectra of direct and strong product graphs
Calculating a product of multiple graphs has been studied in mathematics,
engineering, computer science, and more recently in network science,
particularly in the context of multilayer networks. One of the important
questions to be addressed in this area is how to characterize spectral
properties of a product graph using those of its factor graphs. While several
such characterizations have already been obtained analytically (mostly for
adjacency spectra), characterization of Laplacian spectra of direct product and
strong product graphs has remained an open problem. Here we develop practical
methods to estimate Laplacian spectra of direct and strong product graphs from
spectral properties of their factor graphs using a few heuristic assumptions.
Numerical experiments showed that the proposed methods produced reasonable
estimation with percentage errors confined within a +/-10% range for most
eigenvalues.Comment: 14 pages, 7 figures; to be published in Discrete Applied Mathematic
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