title: Exploring the topological sources of robustness against invasion in biological and technological networks.
abstract: For a network, the accomplishment of its functions despite perturbations is called robustness. Although this property has been extensively studied, in most cases, the network is modified by removing nodes. In our approach, it is no longer perturbed by site percolation, but evolves after site invasion. The process transforming resident/healthy nodes into invader/mutant/diseased nodes is described by the Moran model. We explore the sources of robustness (or its counterpart, the propensity to spread favourable innovations) of the US high-voltage power grid network, the Internet2 academic network, and the C. elegans connectome. We compare them to three modular and non-modular benchmark networks, and samples of one thousand random networks with the same degree distribution. It is found that, contrary to what happens with networks of small order, fixation probability and robustness are poorly correlated with most of standard statistics, but they depend strongly on the degree distribution. While community detection techniques are able to detect the existence of a central core in Internet2, they are not effective in detecting hierarchical structures whose topological complexity arises from the repetition of a few rules. Box counting dimension and Rent's rule are applied to show a subtle trade-off between topological and wiring complexity.
authors: GeoDynApp - ECSING Group (Spain).|Instituto de Matemáticas, Universidade de Santiago de Compostela, E-15782 Santiago de Compostela (Spain).
authors: Fernando
authors: F
authors: Alcalde Cuesta
authors: GeoDynApp - ECSING Group (Spain).|Departamento de Didáctica das Ciencias Experimentais, Facultade de Formación do Profesorado,Universidade de Santiago de Compostela, Avda. Ramón Ferreiro 10, E-27002 Lugo (Spain).
authors: Pablo
authors: P
authors: González Sequeiros
authors: GeoDynApp - ECSING Group (Spain).|Centro Universitario de la Defensa Zaragoza, AGM, Ctra. Huesca s/n. E-50090 Zaragoza (Spain).|Instituto Universitario de Matemáticas y Aplicaciones, Universidad de Zaragoza (Spain).
authors: Álvaro
authors: Á
authors: Lozano Rojo
mesh_terms: D000465:Algorithms; D000818:Animals; D017173:Caenorhabditis elegans / Q000502:physiology*; D008962:Models, Theoretical; D009415:Nerve Net; D011336:Probability