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dc.contributor.authorDickison, Mark E.en_US
dc.date.accessioned2018-10-25T12:47:27Z
dc.date.issued2012
dc.date.submitted2012
dc.identifier.urihttps://hdl.handle.net/2144/31536
dc.descriptionThesis (Ph.D.)--Boston Universityen_US
dc.descriptionPLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at open-help@bu.edu. Thank you.en_US
dc.description.abstractThis thesis employs methods of statistical mechanics and numerical simulations to study some aspects of dynamic and interacting complex networks. The mapping of various social and physical phenomena to complex networks has been a rich field in the past few decades. Subjects as broad as petroleum engineering, scientific collaborations, and the structure of the internet have all been analyzed in a network physics context, with useful and universal results. In the first chapter we introduce basic concepts in networks, including the two types of network configurations that are studied and the statistical physics and epidemiological models that form the framework of the network research, as well as covering various previously-derived results in network theory that are used in the work in the following chapters. In the second chapter we introduce a model for dynamic networks, where the links or the strengths of the links change over time. We solve the model by mapping dynamic networks to the problem of directed percolation, where the direction corresponds to the time evolution of the network. We show that the dynamic network undergoes a percolation phase transition at a critical concentration Pc, that decreases with the rate r at which the network links are changed. The behavior near criticality is universal and independent of r. We find that for dynamic random networks fundamental laws are changed: i) The size of the giant component at criticality scales with the network size N for all values of r, rather than as N^(2/3) in static network, ii) In the presence of a broad distribution of disorder, the optimal path length between two nodes in a dynamic network scales as N^(1/2), compared to N^(1/3) in a static network. The third chapter consists of a study of the effect of quarantine on the propagation of epidemics on an adaptive network of social contacts. For this purpose, we analyze the susceptible-infected-recovered model in the presence of quarantine, where susceptible individuals protect themselves by disconnecting their links to infected neighbors with probability w and reconnecting them to other susceptible individuals chosen at random. Starting from a single infected individual, we show by an analytical approach and simulations that there is a phase transition at a critical rewiring (quarantine) threshold We separating a phase (w < wc) where the disease reaches a large fraction of the population from a phase (w > wc) where the disease does not spread out. We find that in our model the topology of the network strongly affects the size of the propagation and that wc increases with the mean degree and heterogeneity of the network. We also find that wc is reduced if we perform a preferential rewiring, in which the rewiring probability is proportional to the degree of infected nodes. In the fourth chapter, we study epidemic processes on interconnected network systems, and find two distinct regimes. In strongly-coupled network systems, epidemics occur simultaneously across the entire system at a critical value f3e· In contrast, in weakly-coupled network systems, a mixed phase exists below f3e, where an epidemic occurs in one network but does not spread to the coupled network. We derive an expression for the network and disease parameters that allow this mixed phase and verify it numerically. Public health implications of communities comprising these two classes of network systems are also mentioned.en_US
dc.language.isoen_US
dc.publisherBoston Universityen_US
dc.titleDynamic and interacting complex networksen_US
dc.typeThesis/Dissertationen_US
dc.description.embargo2031-01-01
etd.degree.nameDoctor of Philosophyen_US
etd.degree.leveldoctoralen_US
etd.degree.disciplinePhysicsen_US
etd.degree.grantorBoston Universityen_US
dc.identifier.barcode11719032088272
dc.identifier.mmsid99199330940001161


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