| Grant number: | 19/06931-2 |
| Support Opportunities: | Research Grants - Visiting Researcher Grant - International |
| Start date: | September 01, 2019 |
| End date: | April 10, 2020 |
| Field of knowledge: | Physical Sciences and Mathematics - Physics - General Physics |
| Principal Investigator: | Francisco Aparecido Rodrigues |
| Grantee: | Francisco Aparecido Rodrigues |
| Visiting researcher: | José Antonio Méndez-Bermúdez |
| Visiting researcher institution: | Benemérita Universidad Autónoma de Puebla (BUAP) , Mexico |
| Host Institution: | Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil |
| City of the host institution: | São Carlos |
Abstract
In this Project we pretend to introduce and characterize null models, based on random matrix ensembles, for complex networks of current interest. In particular we plan to study: (i) random networks with gain and/or loss, (ii) non-uniform random regular graphs, (iii) bipartite networks, (iv) mutualistic networks, and (v) directed networks. We will perform a scaling analysis to define the universal parameter of each null model, i.e., the parameter that fixes the properties of the cor- responding network model. To this end we will compute quantities commonly used in Random Matrix Theory studies, such as: (i) the nearest- neighbor energy-level spacing distribution, (ii) the distribution of the ratios between nearest-neighbor energy-level spacings, (iii) the average ratio between nearest-neighbor energy-level spacings, (iv) the average Shannon entropy (or average information entropy) of the eigenvectors, and (v) the in- verse participation ratio of the eigenvectors. In all cases we will identify the metallic phase (where the eigenvectors of the null model are extended; so the corresponding network is highly connected), the isolating phase (where the eigenvectors of the null model are localized; so the corresponding network is almost disconnected), as well as the transition regime between both phases. Whenever possible, we will validate our results with data from real-world networks. (AU)
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