| Issue |
EPL
Volume 118, Number 1, April 2017
|
|
|---|---|---|
| Article Number | 18003 | |
| Number of page(s) | 7 | |
| Section | Interdisciplinary Physics and Related Areas of Science and Technology | |
| DOI | https://doi.org/10.1209/0295-5075/118/18003 | |
| Published online | 30 May 2017 | |
Spectral statistics of random geometric graphs
1 School of Mathematics, University of Bristol - University Walk, Bristol BS8 1TW, UK
2 Toshiba Telecommunications Research Laboratory - 32 Queens Square, Bristol BS1 4ND, UK
Received: 24 February 2017
Accepted: 12 May 2017
We use random matrix theory to study the spectrum of random geometric graphs, a fundamental model of spatial networks. Considering ensembles of random geometric graphs we look at short-range correlations in the level spacings of the spectrum via the nearest-neighbour and next-nearest-neighbour spacing distribution and long-range correlations via the spectral rigidity 
statistic. These correlations in the level spacings give information about localisation of eigenvectors, level of community structure and the level of randomness within the networks. We find a parameter-dependent transition between Poisson and Gaussian orthogonal ensemble statistics. That is the spectral statistics of spatial random geometric graphs fits the universality of random matrix theory found in other models such as Erdős-Rényi, Barabási-Albert and Watts-Strogatz random graphs.
PACS: 89.75.Hc – Networks and genealogical trees / 02.10.Yn – Matrix theory / 02.10.Ox – Combinatorics; graph theory
© EPLA, 2017
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