Diffusion and Supercritical Spreading Processes on Complex Networks

Die große Menge an Datensätzen, die in den letzten Jahren verfügbar wurden, hat es ermöglicht, sowohl menschlich-getriebene als auch biologische komplexe Systeme in einem beispiellosen Ausmaß empirisch zu untersuchen. Parallel dazu ist die Vorhersage und Kontrolle epidemischer Ausbrüche für Fragen der öffentlichen Gesundheit sehr wichtig geworden. In dieser Arbeit untersuchen wir einige wichtige Aspekte von Diffusionsphänomenen und Ausbreitungsprozeßen auf Netzwerken. Wir untersuchen drei verschiedene Probleme im Zusammenhang mit Ausbreitungsprozeßen im überkritischen Regime. Zunächst untersuchen wir die Reaktionsdiffusion auf Ensembles zufälliger Netzwerke, die durch die beobachteten Levy-Flugeigenschaften der menschlichen Mobilität charakterisiert sind. Das zweite Problem ist die Schätzung der Ankunftszeiten globaler Pandemien. Zu diesem Zweck leiten wir geeignete verborgene Geometrien netzgetriebener Streuprozeße, unter Nutzung der Random-Walk-Theorie, her und identifizieren diese. Durch die Definition von effective distances wird das Problem komplexer raumzeitlicher Muster auf einfache, homogene Wellenausbreitungsmuster reduziert. Drittens führen wir durch die Einbettung von Knoten in den verborgenen Raum, der durch effective distances im Netzwerk definiert ist, eine neuartige Netzwerkzentralität ein, die ViralRank genannt wird und quantifiziert, wie nahe ein Knoten, im Durchschnitt, den anderen Knoten im Netzwerk ist. Diese drei Studien bilden einen einheitlichen Rahmen zur Charakterisierung von Diffusions- und Ausbreitungsprozeßen, die sich auf komplexen Netzwerken allgemein abzeichnen, und bieten neue Ansätze für herausfordernde theoretische Probleme, die für die Bewertung künftiger Modelle verwendet werden können.The large amount of datasets that became available in recent years has made it possible to empirically study humanly-driven, as well as biological complex systems to an unprecedented extent. In parallel, the prediction and control of epidemic outbreaks have become very important for public health issues. In this thesis, we investigate some important aspects of diffusion phenomena and spreading processes unfolding on networks. We study three different problems related to spreading processes in the supercritical regime. First, we study reaction-diffusion on ensembles of random networks characterized by the observed Levy-flight properties of human mobility. The second problem is the estimation of the arrival times of global pandemics. To this end, we derive and identify suitable hidden geometries of network-driven spreading processes, leveraging on random-walk theory. Through the definition of network effective distances, the problem of complex spatiotemporal patterns is reduced to simple, homogeneous wave propagation patterns. Third, by embedding nodes in the hidden space defined by network effective distances, we introduce a novel network centrality, called ViralRank, which quantifies how close a node is, on average, to the other nodes. These three studies constitute a unified framework to characterize diffusion and spreading processes unfolding on complex networks in very general settings, and provide new approaches to challenging theoretical problems that can be used to benchmark future models

Cold denaturation of RNA secondary structures with loop entropy and quenched disorder

The critical behavior of ribonucleic acid (RNA) secondary structures with quenched sequence randomness is studied by means of the constrained annealing method. A thermodynamic phase transition is induced by including the conformational weight of loop structures. In addition to the expected melting at high temperature, a cold-melting transition appears when the disorder strength induces competition between favorable and unfavorable base pairs. Our results suggest that the cold denaturation of RNA found experimentally might be triggered by quenched sequence disorder. We calculate hot- and cold-melting critical temperatures for competing favorable and unfavorable base-pair energies and present a folding phase diagram as a function of the loop exponent and temperature

Influencers identification in complex networks through reaction-diffusion dynamics

A pivotal idea in network science, marketing research, and innovation diffusion theories is that a small group of nodes—called influencers—have the largest impact on social contagion and epidemic processes in networks. Despite the long-standing interest in the influencers identification problem in socioeconomic and biological networks, there is not yet agreement on which is the best identification strategy. State- of-the-art strategies are typically based either on heuristic centrality measures or on analytic arguments that only hold for specific network topologies or peculiar dynamical regimes. Here, we leverage the recently introduced random-walk effective distance—a topological metric that estimates almost perfectly the arrival time of diffusive spreading processes on networks—to introduce a centrality metric which quantifies how close a node is to the other nodes. We show that the new centrality metric significantly outperforms state-of-the-art metrics in detecting the influencers for global contagion processes. Our findings reveal the essential role of the network effective distance for the influencers identification and lead us closer to the optimal solution of the problem

NMR-Metabolic Methodology in the Study of GM Foods

The 1H-NMR methodology used in the study of genetically modified (GM) foods is discussed. Transgenic lettuce (Lactuca sativa cv "Luxor") over-expressing the ArabidopsisKNAT1 gene is presented as a case study. Twenty-two water-soluble metabolites (amino acids, organic acids, sugars) present in leaves of conventional and GM lettuce were monitored by NMR and quantified at two developmental stages. The NMR spectra did not reveal any difference in metabolite composition between the GM lettuce and the wild type counterpart. Statistical analyses of metabolite variables highlighted metabolism variation as a function of leaf development as well as the transgene. A main effect of the transgene was in altering sugar metabolism

Path-integral formulation of spreading processes in complex networks

We introduce a path-integral formulation of network-based measures that generalize the concept of geodesic distance and that provides fundamental insights into the dynamics of disease transmission as well as an efficient numerical estimation of the infection arrival time

Bi-layer voter model: modeling intolerant/tolerant positions and bots in opinion dynamics

The diffusion of opinions in social networks is a relevant process for adopting positions and attracting potential voters in political campaigns. Opinion polarization, bias, targeted diffusion, and the radicalization of postures are key elements for understanding the voting dynamics’ challenges. In particular, social bots are currently a new element that can have a pronounced effect on the formation of opinions during electoral processes by, for instance, creating fake accounts in social networks to manipulate elections. Here, we propose a voter model incorporating bots and radical or intolerant individuals in the decision-making process. The dynamics of the system occur in a multiplex network of interacting agents composed of two layers, one for the dynamics of opinions where agents choose between two possible alternatives, and the other for the tolerance dynamics, in which agents adopt one of the two tolerance levels. The tolerance accounts for the likelihood to change opinion in an interaction, with tolerant (intolerant) agents switching opinion with probability 1.0 (γ≤ 1). We find that intolerance leads to a consensus of tolerant agents during an initial stage that scales as τ+∼ γ- 1ln N, who then reach an opinion consensus during the second stage in a time that scales as τ∼ N, where N is the number of agents. Therefore, very intolerant agents (γ≪ 1) could considerably slow down dynamics towards the final consensus state. We also find that the inclusion of a fraction σB- of bots breaks the symmetry between both opinions, driving the system to a consensus of intolerant agents with the bots’ opinion. Thus, bots eventually impose their opinion to the entire population, in a time that scales as τB-∼γ-1 for γ≪σB- and τB-∼1/σB- for σB-≪γ.Fil: Vega-Oliveros, Didier A.. Universidade Estadual de Campinas; Brasil. Indiana University; Estados UnidosFil: Grande, Helder L. C.. Instituto Nacional de Pesquisas Espaciais; BrasilFil: Iannelli, Flavio. Universitat Zurich; SuizaFil: Vazquez, Federico. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Calculo. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Calculo; Argentin