Approximating Spectral Impact of Structural Perturbations in Large Networks

Determining the effect of structural perturbations on the eigenvalue spectra of networks is an important problem because the spectra characterize not only their topological structures, but also their dynamical behavior, such as synchronization and cascading processes on networks. Here we develop a theory for estimating the change of the largest eigenvalue of the adjacency matrix or the extreme eigenvalues of the graph Laplacian when small but arbitrary set of links are added or removed from the network. We demonstrate the effectiveness of our approximation schemes using both real and artificial networks, showing in particular that we can accurately obtain the spectral ranking of small subgraphs. We also propose a local iterative scheme which computes the relative ranking of a subgraph using only the connectivity information of its neighbors within a few links. Our results may not only contribute to our theoretical understanding of dynamical processes on networks, but also lead to practical applications in ranking subgraphs of real complex networks.Comment: 9 pages, 3 figures, 2 table

Harmonic analysis on Cayley Trees II: the Bose Einstein condensation

We investigate the Bose-Einstein Condensation on non homogeneous non amenable networks for the model describing arrays of Josephson junctions on perturbed Cayley Trees. The resulting topological model has also a mathematical interest in itself. The present paper is then the application to the Bose-Einstein Condensation phenomena, of the harmonic analysis aspects arising from additive and density zero perturbations, previously investigated by the author in a separate work. Concerning the appearance of the Bose-Einstein Condensation, the results are surprisingly in accordance with the previous ones, despite the lack of amenability. We indeed first show the following fact. Even when the critical density is finite (which is implied in all the models under consideration, thanks to the appearance of the hidden spectrum), if the adjacency operator of the graph is recurrent, it is impossible to exhibit temperature locally normal states (i.e. states for which the local particle density is finite) describing the condensation at all. The same occurs in the transient cases for which it is impossible to exhibit locally normal states describing the Bose--Einstein Condensation at mean particle density strictly greater than the critical density . In addition, for the transient cases, in order to construct locally normal temperature states through infinite volume limits of finite volume Gibbs states, a careful choice of the the sequence of the finite volume chemical potential should be done. For all such states, the condensate is essentially allocated on the base--point supporting the perturbation. This leads that the particle density always coincide with the critical one. It is shown that all such temperature states are Kubo-Martin-Schwinger states for a natural dynamics. The construction of such a dynamics, which is a very delicate issue, is also done.Comment: 28 pages, 6 figures, 1 tabl

Spectral dimension reduction of complex dynamical networks

Dynamical networks are powerful tools for modeling a broad range of complex systems, including financial markets, brains, and ecosystems. They encode how the basic elements (nodes) of these systems interact altogether (via links) and evolve (nodes' dynamics). Despite substantial progress, little is known about why some subtle changes in the network structure, at the so-called critical points, can provoke drastic shifts in its dynamics. We tackle this challenging problem by introducing a method that reduces any network to a simplified low-dimensional version. It can then be used to describe the collective dynamics of the original system. This dimension reduction method relies on spectral graph theory and, more specifically, on the dominant eigenvalues and eigenvectors of the network adjacency matrix. Contrary to previous approaches, our method is able to predict the multiple activation of modular networks as well as the critical points of random networks with arbitrary degree distributions. Our results are of both fundamental and practical interest, as they offer a novel framework to relate the structure of networks to their dynamics and to study the resilience of complex systems.Comment: 16 pages, 8 figure

Social structure contains epidemics and regulates individual roles in disease transmission in a group-living mammal

This is the final version. Available from Wiley via the DOI in this record. Data accessibility: The original weighted adjacency matrix for the high‐density population of European badgers, as well as code used for simulating networks and disease simulations can be found online https://doi.org/10.5061/dryad.49n3878.Population structure is critical to infectious disease transmission. As a result, theoretical and empirical contact network models of infectious disease spread are increasingly providing valuable insights into wildlife epidemiology. Analyzing an exceptionally detailed dataset on contact structure within a high-density population of European badgers Meles meles, we show that a modular contact network produced by spatially structured stable social groups, lead to smaller epidemics, particularly for infections with intermediate transmissibility. The key advance is that we identify considerable variation among individuals in their role in disease spread, with these new insights made possible by the detail in the badger dataset. Furthermore, the important impacts on epidemiology are found even though the modularity of the Badger network is much lower than the threshold that previous work suggested was necessary. These findings reveal the importance of stable social group structure for disease dynamics with important management implications for socially structured populations.Natural Environment Research Council (NERC