Dissertations / Theses on the topic 'Dynamical models on networks'
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1
Tupikina, Liubov. "Temporal and spatial aspects of correlation networks and dynamical network models." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät, 2017. http://dx.doi.org/10.18452/17746.
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In der vorliegenden Arbeit untersuchte ich die komplexen Strukturen von Netzwerken, deren zeitliche Entwicklung, die Interpretationen von verschieden Netzwerk-Massen und die Klassen der Prozesse darauf. Als Erstes leitete ich Masse für die Charakterisierung der zeitlichen Entwicklung der Netzwerke her, um räumlich Veränderungsmuster zu erkennen. Als Nächstes führe ich eine neue Methode zur Konstruktion komplexer Netzwerke von Flussfeldern ein, bei welcher man das Set-up auch rein unter Berufung Berufung auf das Geschwindigkeitsfeld ändern kann. Diese Verfahren wurden für die Korrelationen skalarer Grössen, z. B. Temperatur, entwickelt, welche eine Advektions-Diffusions-Dynamik in der Gegenwart von Zwingen und Dissipation. Die Flussnetzwerk-Methode zur Zeitreihenanalyse konstruiert die Korrelationsmatrizen und komplexen Netzwerke. Dies ermöglicht die Charakterisierung von Transport in Flüssigkeiten, die Identifikation verschiedene Misch-Regimes in dem Fluss und die Anwendung auf die Advektions-DiffusionsDynamik, Klimadaten und anderen Systemen, in denen Teilchentransport eine entscheidende Rolle spielen. Als Letztes, entwickelte ich ein neuartiges Heterogener Opinion Status Modell (HOpS) und Analysetechnik basiert auf Random Walks und Netzwerktopologie Theorien, um dynamischen Prozesse in Netzwerken zu studieren, wie die Verbreitung von Meinungen in sozialen Netzwerken oder Krankheiten in der Gesellschaft. Ein neues Modell heterogener Verbreitung auf einem Netzwerk wird als Beispielssystem für HOpS verwendent, um die vergleichsweise Einfachheit zu nutzen. Die Analyse eines diskreten Phasenraums des HOPS-Modells hat überraschende Eigenschaften, welches sensibel auf die Netzwerktopologie reagieren. Sie können verallgemeinert werden, um verschiedene Klassen von komplexen Netzwerken zu quantifizieren, Transportphänomene zu charakterisieren und verschiedene Zeitreihen zu analysieren.In the thesis I studied the complex architectures of networks, the network evolution in time, the interpretation of the networks measures and a particular class of processes taking place on complex networks. Firstly, I derived the measures to characterize temporal networks evolution in order to detect spatial variability patterns in evolving systems. Secondly, I introduced a novel flow-network method to construct networks from flows, that also allows to modify the set-up from purely relying on the velocity field. The flow-network method is developed for correlations of a scalar quantity (temperature, for example), which satisfies advection-diffusion dynamics in the presence of forcing and dissipation. This allows to characterize transport in the fluids, to identify various mixing regimes in the flow and to apply this method to advection-diffusion dynamics, data from climate and other systems, where particles transport plays a crucial role. Thirdly, I developed a novel Heterogeneous Opinion-Status model (HOpS) and analytical technique to study dynamical processes on networks. All in all, methods, derived in the thesis, allow to quantify evolution of various classes of complex systems, to get insight into physical meaning of correlation networks and analytically to analyze processes, taking place on networks.
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DI, GANGI Domenico. "Models of dynamical networks with applications to finance." Doctoral thesis, Scuola Normale Superiore, 2022. http://hdl.handle.net/11384/112204.
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Preciado, Víctor Manuel. "Spectral analysis for stochastic models of large-scale complex dynamical networks." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45873.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.Includes bibliographical references (p. 179-196).
Research on large-scale complex networks has important applications in diverse systems of current interest, including the Internet, the World-Wide Web, social, biological, and chemical networks. The growing availability of massive databases, computing facilities, and reliable data analysis tools has provided a powerful framework to explore structural properties of such real-world networks. However, one cannot efficiently retrieve and store the exact or full topology for many large-scale networks. As an alternative, several stochastic network models have been proposed that attempt to capture essential characteristics of such complex topologies. Network researchers then use these stochastic models to generate topologies similar to the complex network of interest and use these topologies to test, for example, the behavior of dynamical processes in the network. In general, the topological properties of a network are not directly evident in the behavior of dynamical processes running on it. On the other hand, the eigenvalue spectra of certain matricial representations of the network topology do relate quite directly to the behavior of many dynamical processes of interest, such as random walks, Markov processes, virus/rumor spreading, or synchronization of oscillators in a network. This thesis studies spectral properties of popular stochastic network models proposed in recent years. In particular, we develop several methods to determine or estimate the spectral moments of these models. We also present a variety of techniques to extract relevant spectral information from a finite sequence of spectral moments. A range of numerical examples throughout the thesis confirms the efficacy of our approach. Our ultimate objective is to use such results to understand and predict the behavior of dynamical processes taking place in large-scale networks.
by Víctor Manuel Preciado.
Ph.D.
4
He, Ping. "Robust synchronization of dynamical networks with delay and uncertainty :synthesis & application." Thesis, University of Macau, 2017. http://umaclib3.umac.mo/record=b3691044.
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Nath, Madhurima. "Application of Network Reliability to Analyze Diffusive Processes on Graph Dynamical Systems." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/86841.
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Moore and Shannon's reliability polynomial can be used as a global statistic to explore the behavior of diffusive processes on a graph dynamical system representing a finite sized interacting system. It depends on both the network topology and the dynamics of the process and gives the probability that the system has a particular desired property. Due to the complexity involved in evaluating the exact network reliability, the problem has been classified as a NP-hard problem. The estimation of the reliability polynomials for large graphs is feasible using Monte Carlo simulations. However, the number of samples required for an accurate estimate increases with system size. Instead, an adaptive method using Bernstein polynomials as kernel density estimators proves useful. Network reliability has a wide range of applications ranging from epidemiology to statistical physics, depending on the description of the functionality. For example, it serves as a measure to study the sensitivity of the outbreak of an infectious disease on a network to the structure of the network. It can also be used to identify important dynamics-induced contagion clusters in international food trade networks. Further, it is analogous to the partition function of the Ising model which provides insights to the interpolation between the low and high temperature limits.Ph. D.
The research presented here explores the effects of the structural properties of an interacting system on the outcomes of a diffusive process using Moore-Shannon network reliability. The network reliability is a finite degree polynomial which provides the probability of observing a certain configuration for a diffusive process on networks. Examples of such processes analyzed here are outbreak of an epidemic in a population, spread of an invasive species through international trade of commodities and spread of a perturbation in a physical system with discrete magnetic spins. Network reliability is a novel tool which can be used to compare the efficiency of network models with the observed data, to find important components of the system as well as to estimate the functions of thermodynamic state variables.
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McLoone, Seamus Cornelius. "Nonlinear identification using local model networks." Thesis, Queen's University Belfast, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.326349.
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Gong, Xue. "Dynamical Systems in Cell Division Cycle, Winnerless Competition Models, and Tensor Approximations." Ohio University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1458303716.
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Hellmann, Tim. "Stable networks in static and dynamic models of network formation." Hamburg Kovač, 2009. http://d-nb.info/1001547497/04.
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Li, Caiwei. "Dynamic scheduling of multiclass queueing networks." Diss., Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/24339.
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SIRI, ENRICO. "Dynamic traffic assignment models for disrupted networks." Doctoral thesis, Università degli studi di Genova, 2022. http://hdl.handle.net/11567/1091373.
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Transportation infrastructure systems are one of the cornerstones on which modern societies are founded. They allow the movement of people and goods by enabling business activities, the setting up of supply chains, and they provide access to vital resources and services. It is commonly believed that due to their vast scale and complexity, transportation systems are among the most vulnerable infrastructures in the occurrence of a disruption, i.e. an event that involves extensive damage to people or physical facilities.
The growing awareness about this issue in recent years has led to a growing body of literature on the topic of performance evaluation of transportation networks when affected by disruptive events, aimed at providing adequate estimations of network operability in such contexts. A peculiarity of transportation is to be a socio-technical system where the transportation supply, represented by the infrastructure and related services, interacts with the transportation demand, consisting of all those individuals who access the infrastructure at any given time. This property makes such systems inherently complex and consequently an analysis of their vulnerability in the face of disruptive events needs to be able to account for these interactions. The aim of the present thesis is therefore to develop methodologies capable of convincingly portraying the reaction of such systems in the face of disruptive events by modeling the dynamics that emerges between users and the infrastructure. It is reasonable to assume that travelers due to changed system conditions will adapt their behavior to some degree in order to mitigate the consequences of such events. In this regard, three modeling approaches are presented in this manuscript to address the need to represent this reaction phenomenon.
The first approach involves the use of an inter-period traffic assignment model able to represent the evolution of users' mobility choices in a dynamic context. For each period, the users' reaction is estimated by solving an assignment model thus computing the optimal flow distribution given the current congestion conditions. User habits are taken into account by appropriately limiting the extent of flow redistributions in order to represent the gradual adaptation of the system to the new situation.
Large perturbations can trigger modal shift phenomena between one transport sub-system and another. In this regard, a multi-modal multi-class scenario analysis model is then presented. Railway and road transport sub-networks are thus embedded into an extended hyper-network to model flow exchanges between this two sub-systems. Class-specific assignment models are employed to determine the choice behavior for passenger flows and freight flows. The results of these choices are then routed through the network by means of a discrete-time dynamic flow model.
Finally, the idea that users' behavior may be influenced by their habits is further explored within a path-based inter-period assignment model. It is suggested that users' route choice process is not only influenced by the travel costs of available alternatives but also by users' familiarity with them. More specifically, if changing traffic conditions suddenly make a specific route disadvantageous, users will tend to prefer those that are most topologically similar to the one they are abandoning. This assumption is then investigated by demonstrating that it implies considering a rationally bounded user choice process. The steady state reached by the system as a result of the equilibration process is then detailed and a rigorous proof is provided to show that it is equivalent to a Boundedly Rational User Equilibrium.
All three approaches has been successfully applied on appropriate test networks where a disruption is simulated by altering the network topology.
These models can provide an important contribution to transportation network vulnerability and resilience analyses willing to take into account the interaction between the infrastructure and users.
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Paltrinieri, Federico. "Modeling temporal networks with dynamic stochastic block models." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18805/.
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Osservando il recente interesse per le reti dinamiche temporali e l'ampio numero di campi di applicazione, questa tesi ha due principali propositi: primo, di analizzare alcuni modelli teorici di reti temporali, specialmente lo stochastic blockmodel dinamico, al fine di descrivere la dinamica di sistemi reali e fare previsioni. Il secondo proposito della tesi è quello di creare due nuovi modelli teorici, basati sulla teoria dei processi autoregressivi, dai quali inferire nuovi parametri dalle reti temporali, come la matrice di evoluzione di stato e una migliore stima della varianza del rumore del processo di evoluzione temporale. Infine, tutti i modelli sono testati su un data set interbancario: questi rivelano la presenza di un evento atteso che divide la rete temporale in due periodi distinti con differenti configurazioni e parametri.
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Dalle, Pezze Piero. "Dynamical models of the mammalian target of rapamycin network in ageing." Thesis, University of Newcastle upon Tyne, 2013. http://hdl.handle.net/10443/2183.
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The mammalian Target of Rapamycin (mTOR)kinase is a central regulator of cellular growth and metabolism and plays an important role in ageing and age- related diseases. The increase of invitro data collected to extend our knowledge on its regulation, and consequently improve drug intervention,has highlighted the complexity of the mTOR network. This complexity is also aggravated by the intrinsic time-dependent nature of cellular regulatory network cross-talks and feedbacks. Systems biology constitutes a powerful tool for mathematically for- malising biological networks and investigating such dynamical properties. The present work discusses the development of three dynamical models of the mTOR network. The first aimed at the analysis of the current literature-based hypotheses of mTOR Complex2(mTORC2)regulation. For each hypothesis, the model predicted specific differential dynamics which were systematically tested by invitro experiments. Surprisingly, nocurrent hypothesis could explain the data and a new hypothesis of mTORC2 activation was proposed. The second model extended the previous one with an AMPK module. In this study AMPK was reported to be activated by insulin. Using a hypothesis ranking approach based on model goodness-of-fit, AMPK activity was insilico predicted and in vitro tested to be activated by the insulin receptor substrate(IRS).Finally,the last model linked mTOR with the oxidative stress response, mitochondrial reg- ulation, DNA damage and FoxO transcription factors. This work provided the characterisation of a dynamical mechanism to explain the state transition from normal to senescent cells and their reversibility of the senescentphenotype.
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Tolson, Edward (Edward Thomas) 1980. "Learning models of world dynamics using Bayesian networks." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/87841.
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Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.Includes bibliographical references (leaves 72-74).
by Edward Tolson.
M.Eng.
14
Mazzarisi, Piero. "Dynamic network models with applications to finance." Doctoral thesis, Scuola Normale Superiore, 2019. http://hdl.handle.net/11384/85711.
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This thesis provides new contributions to the field of network models, in two directions. On one hand, we study statistical models of static networks, in particular by contributing to the problem of community detection when link direction
is taken into account, thus identifying what are the macroscopic structures of interest for the problem and the conditions for detectability [Wilinski et al., 2019].
Then, we introduce novel statistical models of dynamic networks which are able
to capture simultaneously latent dynamics for node-specific characteristics together
with link-specific persistence patterns. While the latent dynamics drives the evolution of the network topologies, such as the node degree, i.e. the number of incident
links to the node, or the community structure, i.e. how nodes connect each other in
forming groups, link persistence preserves the past structure of the network. Within
this context, the contribution of the thesis is twofold, both theoretical and empirical [Mazzarisi et al., 2019a, Barucca et al., 2018]. We develop novel methodologies
to disentangle the two linkage mechanisms in order to learn correctly both latent
variables and static parameters of the models. And we consider also applications to
financial data to reveal genuine patterns of persistence, which reflects the role both
nodes and links have in the process of network formation and evolution.
On the other hand, with a focus on the systemic risk of financial systems, we
present a theoretical study of the expectation feedback mechanism which governs the dynamics of a financial network, thus determining its dynamical stability
[Mazzarisi et al., 2019b]. Any financial system is an expectation feedback system:
the current decisions of financial agents depend on what they expect will occur in the
future. Agents’ decisions affect the price dynamics in illiquid markets. Then, when
expectations are formed by using models of past observations, the price dynamics itself feeds back on agents’ expectations. This is in effect a feedback dynamics. Interestingly, the process of expectation formation by agents and the price dynamics act
on different time scales. In our modeling, it is slow for the agents’ expectations and
fast for the price dynamics. Moreover, the agents’ decisions, given the expectations
formed on the basis of the random price dynamics, is to some extent deterministic,
because they represent the optimal portfolio choice in a heavily regulated market.
This separation of time scales is crucial and we are able to characterize analytically
the feedback dynamics in the asymptotic limit of one time scale infinitely larger than
the other one. Hence, we contribute to the research field of systemic risk with the
first analytical proof (to the best of our knowledge) of how expectation feedbacks
in relation to the estimation of investments’ risk and dependencies determine the
dynamical instability of a financial system.
In line with the two research directions, the thesis is divided in two parts. [...]
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Dzalilov, Zari. "Mathematical models of dynamic reconfiguration of telecommunication networks." Thesis, University of Ballarat, 2004. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/58707.
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In this thesis we examine mathematical models for dynamical reconfiguration of telecommunication networks. Dynamical configuration is one of the most important problems in the field of network management. In the case of some part of the network being damaged the system should maintain its operations, taking into account the new conditions. This can be achieved by using the residual capacity of the system if it is available. If there were not enough residual capacity, the managers of the system need to involve some of the unaffected traffic in order to reorganize traffic. A prolonged breakdown will push some customers to change their provider; there is also a lost [sic] of profit because of breakdowns, that can be considered as a certain implicit penalty. To reorganize traffic by using new routes we should have a flexible routing system.Doctor of Philosophy
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Arat, Seda. "A Mathematical Model of a Denitrification Metabolic Network in Pseudomonas aeruginosa." Thesis, Virginia Tech, 2012. http://hdl.handle.net/10919/46208.
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Lake Erie, one of the Great Lakes in North America, has witnessed recurrent summertime low oxygen dead zones for decades. This is a yearly phenomenon that causes microbial production of the greenhouse gas nitrous oxide from denitrification. Complete denitrification is a microbial process of reduction of nitrate to nitrogen gas via nitrite, nitric oxide, and greenhouse gas nitrous oxide. After scanning the microbial community in Lake Erie, Pseudomonas aeruginosa is decided to be examined, not because it is abundant in Lake Erie, but because it can perform denitrification under anaerobic conditions. This study focuses on a mathematical model of the metabolic network in Pseudomonas aeruginosa under denitrification and testable hypotheses generation using polynomial dynamical systems and stochastic discrete dynamical systems. Analysis of the long-term behavior of the system changing the concentration level of oxygen, nitrate, and phosphate suggests that phosphate highly affects the denitrification performance of the network.Master of Science
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Junuthula, Ruthwik Reddy. "Modeling, Evaluation and Analysis of Dynamic Networks for Social Network Analysis." University of Toledo / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1544819215833249.
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Zschaler, Gerd. "Adaptive-network models of collective dynamics." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-89260.
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Complex systems can often be modelled as networks, in which their basic units are represented by abstract nodes and the interactions among them by abstract links. This network of interactions is the key to understanding emergent collective phenomena in such systems. In most cases, it is an adaptive network, which is defined by a feedback loop between the local dynamics of the individual units and the dynamical changes of the network structure itself. This feedback loop gives rise to many novel phenomena. Adaptive networks are a promising concept for the investigation of collective phenomena in different systems. However, they also present a challenge to existing modelling approaches and analytical descriptions due to the tight coupling between local and topological degrees of freedom.
In this thesis, I present a simple rule-based framework for the investigation of adaptive networks, using which a wide range of collective phenomena can be modelled and analysed from a common perspective. In this framework, a microscopic model is defined by the local interaction rules of small network motifs, which can be implemented in stochastic simulations straightforwardly. Moreover, an approximate emergent-level description in terms of macroscopic variables can be derived from the microscopic rules, which we use to analyse the system\'s collective and long-term behaviour by applying tools from dynamical systems theory.
We discuss three adaptive-network models for different collective phenomena within our common framework. First, we propose a novel approach to collective motion in insect swarms, in which we consider the insects\' adaptive interaction network instead of explicitly tracking their positions and velocities. We capture the experimentally observed onset of collective motion qualitatively in terms of a bifurcation in this non-spatial model. We find that three-body interactions are an essential ingredient for collective motion to emerge. Moreover, we show what minimal microscopic interaction rules determine whether the transition to collective motion is continuous or discontinuous.
Second, we consider a model of opinion formation in groups of individuals, where we focus on the effect of directed links in adaptive networks. Extending the adaptive voter model to directed networks, we find a novel fragmentation mechanism, by which the network breaks into distinct components of opposing agents. This fragmentation is mediated by the formation of self-stabilizing structures in the network, which do not occur in the undirected case. We find that they are related to degree correlations stemming from the interplay of link directionality and adaptive topological change.
Third, we discuss a model for the evolution of cooperation among self-interested agents, in which the adaptive nature of their interaction network gives rise to a novel dynamical mechanism promoting cooperation. We show that even full cooperation can be achieved asymptotically if the networks\' adaptive response to the agents\' dynamics is sufficiently fast.
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Jiao, Yue. "Mathematical models for control of probabilistic Boolean networks." Click to view the E-thesis via HKUTO, 2008. http://sunzi.lib.hku.hk/hkuto/record/B41508634.
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Zschaler, Gerd. "Adaptive-network models of collective dynamics." Doctoral thesis, Max-Planck-Institut für Physik komplexer Systeme, 2011. https://tud.qucosa.de/id/qucosa%3A26056.
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Complex systems can often be modelled as networks, in which their basic units are represented by abstract nodes and the interactions among them by abstract links. This network of interactions is the key to understanding emergent collective phenomena in such systems. In most cases, it is an adaptive network, which is defined by a feedback loop between the local dynamics of the individual units and the dynamical changes of the network structure itself. This feedback loop gives rise to many novel phenomena. Adaptive networks are a promising concept for the investigation of collective phenomena in different systems. However, they also present a challenge to existing modelling approaches and analytical descriptions due to the tight coupling between local and topological degrees of freedom.
In this thesis, I present a simple rule-based framework for the investigation of adaptive networks, using which a wide range of collective phenomena can be modelled and analysed from a common perspective. In this framework, a microscopic model is defined by the local interaction rules of small network motifs, which can be implemented in stochastic simulations straightforwardly. Moreover, an approximate emergent-level description in terms of macroscopic variables can be derived from the microscopic rules, which we use to analyse the system\'s collective and long-term behaviour by applying tools from dynamical systems theory.
We discuss three adaptive-network models for different collective phenomena within our common framework. First, we propose a novel approach to collective motion in insect swarms, in which we consider the insects\' adaptive interaction network instead of explicitly tracking their positions and velocities. We capture the experimentally observed onset of collective motion qualitatively in terms of a bifurcation in this non-spatial model. We find that three-body interactions are an essential ingredient for collective motion to emerge. Moreover, we show what minimal microscopic interaction rules determine whether the transition to collective motion is continuous or discontinuous.
Second, we consider a model of opinion formation in groups of individuals, where we focus on the effect of directed links in adaptive networks. Extending the adaptive voter model to directed networks, we find a novel fragmentation mechanism, by which the network breaks into distinct components of opposing agents. This fragmentation is mediated by the formation of self-stabilizing structures in the network, which do not occur in the undirected case. We find that they are related to degree correlations stemming from the interplay of link directionality and adaptive topological change.
Third, we discuss a model for the evolution of cooperation among self-interested agents, in which the adaptive nature of their interaction network gives rise to a novel dynamical mechanism promoting cooperation. We show that even full cooperation can be achieved asymptotically if the networks\' adaptive response to the agents\' dynamics is sufficiently fast.
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Rohden, Martin [Verfasser], Marc [Akademischer Betreuer] Timme, Tim [Akademischer Betreuer] Friede, and Reiner [Akademischer Betreuer] Kree. "Synchronization and Stability in Dynamical Models of Power Supply Networks / Martin Rohden. Gutachter: Tim Friede ; Reiner Kree. Betreuer: Marc Timme." Göttingen : Niedersächsische Staats- und Universitätsbibliothek Göttingen, 2015. http://d-nb.info/106504478X/34.
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Jiao, Yue, and 焦月. "Mathematical models for control of probabilistic Boolean networks." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B41508634.
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Boulaire, Fanny A. "Compositional agent-based models for electricity distribution networks." Thesis, Queensland University of Technology, 2015. https://eprints.qut.edu.au/90054/12/90054%28thesis%29.pdf.
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This thesis presents a novel approach to building large-scale agent-based models of networked physical systems using a compositional approach to provide extensibility and flexibility in building the models and simulations. A software framework (MODAM - MODular Agent-based Model) was implemented for this purpose, and validated through simulations. These simulations allow assessment of the impact of technological change on the electricity distribution network looking at the trajectories of electricity consumption at key locations over many years.
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KC, Rabi. "Study of Some Biologically Relevant Dynamical System Models: (In)stability Regions of Cyclic Solutions in Cell Cycle Population Structure Model Under Negative Feedback and Random Connectivities in Multitype Neuronal Network Models." Ohio University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou16049254273607.
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Tupikina, Liubov [Verfasser], Jürgen [Gutachter] Kurths, Lutz [Gutachter] Schimansky-Geier, and Sergei [Gutachter] Nechaev. "Temporal and spatial aspects of correlation networks and dynamical network models : analytical approaches and physical applications / Liubov Tupikina ; Gutachter: Jürgen Kurths, Lutz Schimansky-Geier, Sergei Nechaev." Berlin : Mathematisch-Naturwissenschaftliche Fakultät, 2017. http://d-nb.info/1130698483/34.
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Ojha, Hem Raj. "Link Dynamics in Student Collaboration Networks using Schema Based Structured Network Models on Canvas LMS." Miami University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=miami1596154905454069.
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Halnes, Geir. "Biological network modelling : relating structure and dynamics to function in food webs and neural networks /." Uppsala : Dept. of Biometry and Engineering, Swedish University of Agricultural Sciences, 2007. http://epsilon.slu.se/2007113.pdf.
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Woodbury, Nathan Scott. "Representation and Reconstruction of Linear, Time-Invariant Networks." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7402.
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Network reconstruction is the process of recovering a unique structured representation of some dynamic system using input-output data and some additional knowledge about the structure of the system. Many network reconstruction algorithms have been proposed in recent years, most dealing with the reconstruction of strictly proper networks (i.e., networks that require delays in all dynamics between measured variables). However, no reconstruction technique presently exists capable of recovering both the structure and dynamics of networks where links are proper (delays in dynamics are not required) and not necessarily strictly proper.The ultimate objective of this dissertation is to develop algorithms capable of reconstructing proper networks, and this objective will be addressed in three parts. The first part lays the foundation for the theory of mathematical representations of proper networks, including an exposition on when such networks are well-posed (i.e., physically realizable). The second part studies the notions of abstractions of a network, which are other networks that preserve certain properties of the original network but contain less structural information. As such, abstractions require less a priori information to reconstruct from data than the original network, which allows previously-unsolvable problems to become solvable. The third part addresses our original objective and presents reconstruction algorithms to recover proper networks in both the time domain and in the frequency domain.
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Kooner, Priya. "Mathematical modelling of tumour invasion : from biochemical networks to tissue dynamics." Thesis, University of Oxford, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.670187.
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FLEISCHMAN, GREGORY JOSEPH. "FLUID FILTRATION FROM CAPILLARY NETWORKS (MICROCIRCULATION, MATHEMATICAL MODELING)." Diss., The University of Arizona, 1985. http://hdl.handle.net/10150/187998.
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A mathematical model has been developed which describes the fluid exchange from a capillary network of realistic topology, and calculates the spatial distribution of extravascular pressure. In this model, the capillaries are represented by a superposition of sources and sinks, resulting from a D'Arcy's Law description of flow in tissue of uniform fluid conductivity. The combination of this representation and Starling's Hypothesis, which relates the forces influencing transmural fluid exchange, yields an integral equation of the second kind which is solved numerically for the source strength distribution. Two important features of this approach are that: (i) it allows for interaction between the local tissue pressure field and fluid exchange (the model is called, therefore, the tissue pressure interaction model); and (ii) complex network morphologies are easily modeled. In single capillaries, this interaction, which decreases the predicted fluid exchange, increases with the magnitude of the ratio of capillary wall to extravascular fluid conductivities. For multiple capillaries, in addition to the "self" interaction of a capillary with the local extravascular pressure field, there is the possibility of interaction between capillaries ("capillary-capillary" interaction). The ratio of conductivities, and the additional factors of intercapillary distance and the number of capillaries, also affect interaction in capillary networks. Although interaction is only a weak function of intercapillary distance, it depends strongly on the number of capillaries. The major result from this work is that for the entire physiological range of conductivity ratios, interaction cannot be neglected in predicting fluid exchange. Although tissue pressure interaction affects the magnitude of fluid exchange, it does not greatly alter the pattern of extravascular flow. Therefore, previous models which neglected interaction are not invalidated by the present findings. The effect of interaction on planar capillary networks within a semi-infinite tissue space was also investigated. Flow boundary conditions were imposed at opposed planar boundaries, parallel with the capillary network. Interaction was found to decrease with decreasing distance between the boundary and plane of the capillaries. It still exerted a large effect, however, for distances greater than one-fourth the reference capillary length.
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Colombini, Giulio. "Synchronisation phenomena in complex neuronal networks." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23904/.
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The phenomenon of neural synchronisation, a simultaneous and repeated firing of clusters of neurons, underlies many physiological functions and pathological manifestations in the brain of humans and animals, ranging from information encoding to epileptic seizures. Neural synchronisation, as a general phenomenon, can be approached theoretically in the framework of Dynamical Systems on Networks. In the present work, we do so by considering complex networks of FitzHugh-Nagumo model neurons. In the first part we consider the most understood models where each neuron treats its presynaptic neurons all on an equal footing, normalising signals with its in-degree. We study the stability of the synchronous state by devising an algorithm that destabilises it by selecting and removing links from the network, so to obtain a bipartite network. The selection is performed using a perturbative expression, which can be regarded as a specialisation of a previously introduced Spectral Centrality measure. The algorithm is tested on Erdős-Renyi, Watts-Strogatz and Barabási-Albert networks, and its behaviour is assessed from a dynamical and from a structural point of view. In the second part we consider the less studied case in which each neuron divides equally its output among the postsynaptic neurons, so to reproduce schematically the situation where a fixed quantity of neurotransmitter is subdivided between several efferent neurons. In this context a self-consistent approach is formulated and its limitations are explored. In order to extend its application to larger networks, a Mean Field Approximation is presented. The predictivity of the Mean Field Approach is then tested on the different random network models, and the results are discussed in terms of the original network properties.
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Iyer, Bharat Vishwanathan. "Capacity and scale-free dynamics of evolving wireless networks." Thesis, Texas A&M University, 2003. http://hdl.handle.net/1969.1/1359.
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Many large-scale random graphs (e.g., the Internet) exhibit complex topology, nonhomogeneous spatial node distribution, and preferential attachment of new nodes. Current topology models for ad-hoc networks mostly consider a uniform spatial distribution of nodes and do not capture the dynamics of evolving, real-world graphs, in which nodes "gravitate" toward popular locations and self-organize into non-uniform clusters. In this thesis, we first investigate two constraints on scalability of ad-hoc networks network reliability and node capacity. Unlike other studies, we analyze network resilience to node and link failure with an emphasis on the growth (i.e., evolution) dynamics of the entire system. Along the way, we also study important graph-theoretic properties of ad-hoc networks (including the clustering coefficient and the expected path length) and strengthen our generic understanding of these systems. Finally, recognizing that under existing uniform models future ad-hoc networks cannot scale beyond trivial sizes, we argue that ad-hoc networks should be modeled from an evolution standpoint, which takes into account the well-known "clustering" phenomena observed in all real-world graphs. This model is likely to describe how future ad-hoc networks will self-organize since it is well documented that information content distribution among end-users (as well as among spatial locations) is non-uniform (often heavy-tailed). Results show that node capacity in the proposed evolution model scales to larger network sizes than in traditional approaches, which suggest that non-uniformly clustered, self-organizing, very large-scale ad-hoc networks may become feasible in the future.
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Kulkarni, Anirudh. "Dynamics of neuronal networks." Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066377/document.
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Dans cette thèse, nous étudions le vaste domaine des neurosciences à travers des outils théoriques, numériques et expérimentaux. Nous étudions comment les modèles à taux de décharge peuvent être utilisés pour capturer différents phénomènes observés dans le cerveau. Nous étudions les régimes dynamiques des réseaux couplés de neurones excitateurs (E) et inhibiteurs (I): Nous utilisons une description fournie par un modèle à taux de décharge et la comparons avec les simulations numériques des réseaux de neurones à potentiel d'action décrits par le modèle EIF. Nous nous concentrons sur le régime où le réseau EI présente des oscillations, puis nous couplons deux de ces réseaux oscillants pour étudier la dynamique résultante. La description des différents régimes pour le cas de deux populations est utile pour comprendre la synchronisation d'une chaine de modules E-I et la propagation d'ondes observées dans le cerveau. Nous examinons également les modèles à taux de décharge pour décrire l'adaptation sensorielle: Nous proposons un modèle de ce type pour décrire l'illusion du mouvement consécutif («motion after effect», (MAE)) dans la larve du poisson zèbre. Nous comparons le modèle à taux de décharge avec des données neuronales et comportementales nouvellesIn this thesis, we investigate the vast field of neuroscience through theoretical, numerical and experimental tools. We study how rate models can be used to capture various phenomena observed in the brain. We study the dynamical regimes of coupled networks of excitatory (E) and inhibitory neurons (I) using a rate model description and compare with numerical simulations of networks of neurons described by the EIF model. We focus on the regime where the EI network exhibits oscillations and then couple two of these oscillating networks to study the resulting dynamics. The description of the different regimes for the case of two populations is helpful to understand the synchronization of a chain of E-I modules and propagation of waves observed in the brain. We also look at rate models of sensory adaptation. We propose one such model to describe the illusion of motion after effect in the zebrafish larva. We compare this rate model with newly obtained behavioural and neuronal data in the zebrafish larva
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Laughton, Stephen Nicholas. "Dynamics of neural networks and disordered spin systems." Thesis, University of Oxford, 1995. http://ora.ox.ac.uk/objects/uuid:5531cef6-4682-4750-9c5c-cb69e5e72d64.
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I obtain a number of results for the dynamics of several disordered spin systems, of successively greater complexity. I commence with the generalised Hopfield model trained with an intensive number of patterns, where in the thermodynamic limit macroscopic, deterministic equations of motion can be derived exactly for both the synchronous discrete time and asynchronous continuous time dynamics. I show that for symmetric embedding matrices Lyapunov functions exist at the macroscopic level of description in terms of pattern overlaps. I then show that for asymmetric embedding matrices several types of bifurcation phenomena to complex non-transient dynamics occur, even in this simplest model. Extending a recent result of Coolen and Sherrington, I show how the dynamics of the generalised Hopfield model trained with extensively many patterns and non-trivial embedding matrix can be described by the evolution of a small number of overlaps and the disordered contribution to the 'energy', upon calculation of a noise distribution by the replica method. The evaluation of the noise distribution requires two key assumptions: that the flow equations are self averaging, and that equipartitioning of probability occurs within the macroscopic sub-shells of the ensemble. This method is inexact on intermediate time scales, due to the microscopic information integrated out in order to derive a closed set of equations. I then show how this theory can be improved in a systematic manner by introducing an order parameter function - the joint distribution of spins and local alignment fields, which evolves in time deterministically, according to a driven diffusion type equation. I show how the coefficients in this equation can be evaluated for the generalised Sherrington-Kirkpatrick model, both within the replica symmetric ansatz, and using Parisi's ultrametric ansatz for the replica matrices, upon making once again the two key assumptions (self averaging and equipartitioning). Since the order parameter is now a continuous function, however, the assumption of equipartitioning within the macroscopic sub-shells is much less restricting.
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Chaves, Madalena. "Predictive analysis of dynamical systems: combining discrete and continuous formalisms." Habilitation à diriger des recherches, Université Nice Sophia Antipolis, 2013. http://tel.archives-ouvertes.fr/tel-00908927.
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The mathematical analysis of dynamical systems covers a wide range of challenging problems related to the time evolution, transient and asymptotic behavior, or regulation and control of physical systems. A large part of my work has been motivated by new mathematical questions arising from biological systems, especially signaling and genetic regulatory networks, where the classical methods usually don't directly apply. Problems include parameter estimation, robustness of the system, model reduction, or model assembly from smaller modules, or control of a system towards a desired state. Although many different formalisms and methodologies can be used to study these problems, in the past decade my work has focused on discrete and hybrid modeling frameworks with the goal of developing intuitive, computationally amenable, and mathematically rigorous, methods of analysis. Discrete (and, in particular, Boolean) models involve a high degree of abstraction and provide a qualitative description of the systems' dynamics. Such models are often suitable to represent the known interactions in gene regulatory networks and their advantage is that a large range of theoretical analysis tools are available using, for instance, graph theoretical concepts. Hybrid (piecewise affine) models have discontinuous vector fields but provide a continuous and more quantitative description of the dynamics. These systems can be analytically studied in each region of an appropriate partition of the state space, and the full solution given as a concatenation of the solutions in each region. Here, I will introduce the two formalisms and then, using several examples, illustrate how a combination of different formalisms permits comparison of results, as well as gaining quantitative knowledge and predictive power on a biological system, through the use of complementary mathematical methods.
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Ahn, Sungwoo. "Transient and Attractor Dynamics in Models for Odor Discrimination." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1280342970.
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Moller, Karl. "Dynamics of an active crosslinker on a chain and aspects of the dynamics of polymer networks." Thesis, Stellenbosch : Stellenbosch University, 2011. http://hdl.handle.net/10019.1/18001.
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Thesis (MSc)--Stellenbosch University, 2011.ENGLISH ABSTRACT: Active materials are a subset of soft matter that is constantly being driven out of an equilibrium state due to the energy input from internal processes such as the hydrolysis of adenosine triphosphate (ATP) to adenosine diphosphate (ADP), as found in biological systems. Firstly, we construct and study a simple model of a flexible filament with an active crosslinker/molecular motor. We treat the system on a mesoscopic scale using a Langevin equation approach, which we analyse via a functional integral approach using the Martin-Siggia-Rose formalism. We characterise the steady state behaviour of the system up to first order in the motor force and also the autocorrelation of fluctuations of the position of the active crosslink on the filament. We find that this autocorrelation function does not depend on the motor force up to first order for the case where the crosslinker is located in the middle of the contour length of the filament. Properties that characterise the elastic response of the system are studied and found to scale with the autocorrelation of fluctuations of the active crosslink position. Secondly, we give a brief overview of the current state of dynamical polymer network theory and then propose two dynamical network models based on a Cayley-tree topology. Our first model takes a renormalisation approach and derive recurrence relations for the coupling constants of the system. The second model builds on the ideas of an Edwards type network theory where Wick’s theorem is employed to enforce the constraint conditions. Both models are examined using a functional integral approach.
AFRIKAANSE OPSOMMING: Aktiewe stelsels is ’n subveld van sagte materie fisika wat handel oor sisteme wat uit ekwilibruim gedryf word deur middel van interne prossesse, soos wat gevind word in biologiese stelsels. Eerstens konstruëer en bestudeer ons ’n model vir ’n buigbare filament met ’n aktiewe kruisskakelaar of molekulêre motor. Ons formuleer die stelsel op ’n mesoskopiese skaal deur gebruik te maak van ’n Langevin vergelyking formalisme en bestudeer die stelsel deur gebruik te maak van funksionaal integraal metodes deur middel van die Martin-Siggia-Rose formalisme. Dit laat ons in staat om die tydonafhankle gedrag van die stelsel te bestudeer tot op eerste orde in die motorkrag. Ons is ook in staat om die outokorrelasie fluktuasies van die posisie van die aktiewe kruisskakelaar te karakteriseer. Ons vind dat die outokorrelasie onafhanklink is van die motorkrag tot eerste orde in die geval waar die kruisskakelaar in die middel van die filament geleë is. Die elastiese eienksappe van die sisteem word ook ondersoek en gevind dat die skaleer soos die outokorrelasie van die fluktuasies van die aktiewe kruisskakelaar posisie. Tweedens gee ons ’n vlugtige oorsig van die huidige toestand van dinamiese polimeer netwerk teorie en stel dan ons eie twee modelle voor wat gebasseer is op ’n Caylee-boom topologie. Ons eerste model maak gebruik van ’n hernormering beginsel en dit laat ons toe om rekurrensierelasies vir die koppelingskonstates te verkry. Die tweede model bou op idees van ’n Edwards tipe netwerk teorie waar Wick se teorema ingespan word om die beperkingskondisies af te dwing. Beide modelle word met funksionaal integraal metodes bestudeer.
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Dimitrova, Elena Stanimirova. "Polynomial Models for Systems Biology: Data Discretization and Term Order Effect on Dynamics." Diss., Virginia Tech, 2006. http://hdl.handle.net/10919/28490.
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Systems biology aims at system-level understanding of biological systems, in particular cellular networks. The milestones of this understanding are knowledge of the structure of the system, understanding of its dynamics, effective control methods, and powerful prediction capability. The complexity of biological systems makes it inevitable to consider mathematical modeling in order to achieve these goals.
The enormous accumulation of experimental data representing the activities of the living cell has triggered an increasing interest in the reverse engineering of biological networks from data. In particular, construction of discrete models for reverse engineering of biological networks is receiving attention, with the goal of providing a coarse-grained description of such networks. In this dissertation we consider the modeling framework of polynomial dynamical systems over finite fields constructed from experimental data. We present and propose solutions to two problems inherent in this modeling method: the necessity of appropriate discretization of the data and the selection of a particular polynomial model from the set of all models that fit the data.
Data discretization, also known as binning, is a crucial issue for the construction of discrete models of biological networks. Experimental data are however usually continuous, or, at least, represented by computer floating point numbers. A major challenge in discretizing biological data, such as those collected through microarray experiments, is the typically small samples size. Many methods for discretization are not applicable due to the insufficient amount of data. The method proposed in this work is a first attempt to develop a discretization tool that takes into consideration the issues and limitations that are inherent in short data time courses. Our focus is on the two characteristics that any discretization method should possess in order to be used for dynamic modeling: preservation of dynamics and information content and inhibition of noise.
Given a set of data points, of particular importance in the construction of polynomial models for the reverse engineering of biological networks is the collection of all polynomials that vanish on this set of points, the so-called ideal of points. Polynomial ideals can be represented through a special finite generating set, known as Gröbner basis, that possesses some desirable properties. For a given ideal, however, the Gröbner basis may not be unique since its computation depends on the choice of leading terms for the multivariate polynomials in the ideal. The correspondence between data points and uniqueness of Gröbner bases is studied in this dissertation. More specifically, an algorithm is developed for finding all minimal sets of points that, added to the given set, have a corresponding ideal of points with a unique Gröbner basis. This question is of interest in itself but the main motivation for studying it was its relevance to the construction of polynomial dynamical systems.
This research has been partially supported by NIH Grant Nr. RO1GM068947-01.Ph. D.
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Leung, Chi Ho. "Necessary and Sufficient Conditions on State Transformations That Preserve the Causal Structure of LTI Dynamical Networks." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7413.
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Linear time-invariant (LTI) dynamic networks are described by their dynamical structure function, and generally, they have many possible state space realizations. This work characterizes the necessary and sufficient conditions on a state transformation that preserves the dynamical structure function, thereby generating the entire set of realizations of a given order for a specific dynamic network.
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Cami, Aurel. "ANALYZING THE COMMUNITY STRUCTURE OF WEB-LIKE NETWORKS: MODELS AND ALGORITHMS." Doctoral diss., University of Central Florida, 2005. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2590.
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This dissertation investigates the community structure of web-like networks (i.e., large, random, real-life networks such as the World Wide Web and the Internet). Recently, it has been shown that many such networks have a locally dense and globally sparse structure with certain small, dense subgraphs occurring much more frequently than they do in the classical Erdös-Rényi random graphs. This peculiarity--which is commonly referred to as community structure--has been observed in seemingly unrelated networks such as the Web, email networks, citation networks, biological networks, etc. The pervasiveness of this phenomenon has led many researchers to believe that such cohesive groups of nodes might represent meaningful entities. For example, in the Web such tightly-knit groups of nodes might represent pages with a common topic, geographical location, etc., while in the neural networks they might represent evolved computational units. The notion of community has emerged in an effort to formalize the empirical observation of the locally dense globally sparse structure of web-like networks. In the broadest sense, a community in a web-like network is defined as a group of nodes that induces a dense subgraph which is sparsely linked with the rest of the network. Due to a wide array of envisioned applications, ranging from crawlers and search engines to network security and network compression, there has recently been a widespread interest in finding efficient community-mining algorithms. In this dissertation, the community structure of web-like networks is investigated by a combination of analytical and computational techniques: First, we consider the problem of modeling the web-like networks. In the recent years, many new random graph models have been proposed to account for some recently discovered properties of web-like networks that distinguish them from the classical random graphs. The vast majority of these random graph models take into account only the addition of new nodes and edges. Yet, several empirical observations indicate that deletion of nodes and edges occurs frequently in web-like networks. Inspired by such observations, we propose and analyze two dynamic random graph models that combine node and edge addition with a uniform and a preferential deletion of nodes, respectively. In both cases, we find that the random graphs generated by such models follow power-law degree distributions (in agreement with the degree distribution of many web-like networks). Second, we analyze the expected density of certain small subgraphs--such as defensive alliances on three and four nodes--in various random graphs models. Our findings show that while in the binomial random graph the expected density of such subgraphs is very close to zero, in some dynamic random graph models it is much larger. These findings converge with our results obtained by computing the number of communities in some Web crawls. Next, we investigate the computational complexity of the community-mining problem under various definitions of community. Assuming the definition of community as a global defensive alliance, or a global offensive alliance we prove--using transformations from the dominating set problem--that finding optimal communities is an NP-complete problem. These and other similar complexity results coupled with the fact that many web-like networks are huge, indicate that it is unlikely that fast, exact sequential algorithms for mining communities may be found. To handle this difficulty we adopt an algorithmic definition of community and a simpler version of the community-mining problem, namely: find the largest community to which a given set of seed nodes belong. We propose several greedy algorithms for this problem: The first proposed algorithm starts out with a set of seed nodes--the initial community--and then repeatedly selects some nodes from community's neighborhood and pulls them in the community. In each step, the algorithm uses clustering coefficient--a parameter that measures the fraction of the neighbors of a node that are neighbors themselves--to decide which nodes from the neighborhood should be pulled in the community. This algorithm has time complexity of order , where denotes the number of nodes visited by the algorithm and is the maximum degree encountered. Thus, assuming a power-law degree distribution this algorithm is expected to run in near-linear time. The proposed algorithm achieved good accuracy when tested on some real and computer-generated networks: The fraction of community nodes classified correctly is generally above 80% and often above 90% . A second algorithm based on a generalized clustering coefficient, where not only the first neighborhood is taken into account but also the second, the third, etc., is also proposed. This algorithm achieves a better accuracy than the first one but also runs slower. Finally, a randomized version of the second algorithm which improves the time complexity without affecting the accuracy significantly, is proposed. The main target application of the proposed algorithms is focused crawling--the selective search for web pages that are relevant to a pre-defined topic.Ph.D.
School of Computer Science
Engineering and Computer Science
Computer Science
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Delitzscher, Sascha [Verfasser]. "Random networks, threshold models and social dynamics / Sascha Delitzscher. Fakultät für Physik." Bielefeld : Universitätsbibliothek Bielefeld, Hochschulschriften, 2012. http://d-nb.info/1019576111/34.
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Kolgushev, Oleg. "Influence of Underlying Random Walk Types in Population Models on Resulting Social Network Types and Epidemiological Dynamics." Thesis, University of North Texas, 2016. https://digital.library.unt.edu/ark:/67531/metadc955128/.
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Epidemiologists rely on human interaction networks for determining states and dynamics of disease propagations in populations. However, such networks are empirical snapshots of the past. It will greatly benefit if human interaction networks are statistically predicted and dynamically created while an epidemic is in progress. We develop an application framework for the generation of human interaction networks and running epidemiological processes utilizing research on human mobility patterns and agent-based modeling. The interaction networks are dynamically constructed by incorporating different types of Random Walks and human rules of engagements. We explore the characteristics of the created network and compare them with the known theoretical and empirical graphs. The dependencies of epidemic dynamics and their outcomes on patterns and parameters of human motion and motives are encountered and presented through this research. This work specifically describes how the types and parameters of random walks define properties of generated graphs. We show that some configurations of the system of agents in random walk can produce network topologies with properties similar to small-world networks. Our goal is to find sets of mobility patterns that lead to empirical-like networks. The possibility of phase transitions in the graphs due to changes in the parameterization of agent walks is the focus of this research as this knowledge can lead to the possibility of disruptions to disease diffusions in populations. This research shall facilitate work of public health researchers to predict the magnitude of an epidemic and estimate resources required for mitigation.
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Gargesa, Padmashri. "Reward-driven Training of Random Boolean Network Reservoirs for Model-Free Environments." PDXScholar, 2013. https://pdxscholar.library.pdx.edu/open_access_etds/669.
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Reservoir Computing (RC) is an emerging machine learning paradigm where a fixed kernel, built from a randomly connected "reservoir" with sufficiently rich dynamics, is capable of expanding the problem space in a non-linear fashion to a higher dimensional feature space. These features can then be interpreted by a linear readout layer that is trained by a gradient descent method. In comparison to traditional neural networks, only the output layer needs to be trained, which leads to a significant computational advantage. In addition, the short term memory of the reservoir dynamics has the ability to transform a complex temporal input state space to a simple non-temporal representation. Adaptive real-time systems are multi-stage decision problems that can be used to train an agent to achieve a preset goal by performing an optimal action at each timestep. In such problems, the agent learns through continuous interactions with its environment. Conventional techniques to solving such problems become computationally expensive or may not converge if the state-space being considered is large, partially observable, or if short term memory is required in optimal decision making. The objective of this thesis is to use reservoir computers to solve such goal-driven tasks, where no error signal can be readily calculated to apply gradient descent methodologies. To address this challenge, we propose a novel reinforcement learning approach in combination with reservoir computers built from simple Boolean components. Such reservoirs are of interest because they have the potential to be fabricated by self-assembly techniques. We evaluate the performance of our approach in both Markovian and non-Markovian environments. We compare the performance of an agent trained through traditional Q-Learning. We find that the reservoir-based agent performs successfully in these problem contexts and even performs marginally better than Q-Learning agents in certain cases. Our proposed approach allows to retain the advantage of traditional parameterized dynamic systems in successfully modeling embedded state-space representations while eliminating the complexity involved in training traditional neural networks. To the best of our knowledge, our method of training a reservoir readout layer through an on-policy boot-strapping approach is unique in the field of random Boolean network reservoirs.
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Abufouda, Mohammed [Verfasser], and Katharina [Akademischer Betreuer] Zweig. "Learning From Networked-data: Methods and Models for Understanding Online Social Networks Dynamics / Mohammed Abufouda ; Betreuer: Katharina Zweig." Kaiserslautern : Technische Universität Kaiserslautern, 2020. http://d-nb.info/1221599747/34.
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MANFREDOTTI, CRISTINA ELENA. "Modeling and inference with relational dynamic bayesian networks." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2010. http://hdl.handle.net/10281/7829.
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Many domains in the real world are richly structured, containing a diverse set of agents characterized by different set of features and related to each other in a variety of ways. Moreover, uncertainty both on the objects observations and on their relations can be present. This is the case of many problems as, for example, multi-target tracking, activity recognition, automatic surveillance and traffic monitoring.
The common ground of these types of problems is the necessity of recognizing and understanding the scene, the activities that are going on, who are the actors, their role and estimate their positions. When the environment is particularly complex, including several distinct entities
whose behaviors might be correlated, automated reasoning becomes particularly challenging. Even in cases where humans can easily recognize activities, current computer programs fail because they lack of commonsense reasoning, and because the current limitation of automated reasoning systems. As a result surveillance supervision is so far mostly delegated to humans. The explicit representation of the interconnected behaviors of agents can provide better models for capturing key elements of the activities in the scene. In this Thesis we propose the use of relations to model particular correlations between agents features, aimed at improving the inference task. We propose the use of relational Dynamic Bayesian Networks, an extension of Dynamic Bayesian Networks with First Order Logic, to represent the dependencies between an agent’s attributes, the scene’s elements and the evolution of state variables over time. In this way, we can combine the advantages of First Order Logic (that can compactly represent structured environments), with those of probabilistic models (that provide a mathematically sound framework for inference in face of uncertainty).
In particular, we investigate the use of Relational Dynamic Bayesian Networks to represent the dependencies between the agents’ behaviors in the context of multi-agents tracking and activity recognition. We propose a new formulation of the transition model that accommodates for relations and present a filtering algorithm that extends the Particle Filter algorithm in order to directly track relations between the agents.
The explicit recognition of the relationships between interacting objects can improve the understanding of their dynamic domain. The inference algorithm we develop in this Thesis is able to take into account relations between interacting objects and we demonstrate with experiments
that the performance of our relational approach outperforms those of standard non-relational methods.
While the goal of emulating human-level inference on scene understanding is out of reach for the current state of the art, we believe that this work represents an important step towards better algorithms and models to provide inference in complex multi-agent systems.
Another advantage of our probabilistic model is its ability to make inference online, so that the appropriate cause of action can be taken when necessary (e.g., raise an alarm). This is an important requirement for the adoption of automatic surveillance systems in the real world, and avoid the common problems associated with human surveillance.
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Eaton, Carrie Elizabeth Diaz. "Ion Channel Dynamics in Interneuron Models of the Cricket Cercal Sensory System." Fogler Library, University of Maine, 2004. http://www.library.umaine.edu/theses/pdf/EatonCED2004.pdf.
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Anacleto, Junior Osvaldo. "Bayesian dynamic graphical models for high-dimensional flow forecasting in road traffic networks." Thesis, Open University, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.594253.
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Congestion on roads is a crucial problem which affects our lives in many ways: As a consequence, there is a strong effort to improve road networks in order to keep the traffic flowing. Flow forecasting models based on the large amount of traffic data, which are now available, can be a very useful tool to support decisions and actions when managing traffic networks. Although many forecasting models have been developed to this end, very few of them capture important features of high dimensional traffic data and, moreover, operating most of these models is a hard task when considering on-line traffic management environments. Dynamic graphical models can be a suitable choice to address the challenge of forecasting high-dimensional traffic flows in real-time. These models represent network flows by a graph, which not only is a useful pictorial representation of multivariate time series of traffic flow data, but it also ensures that model computation is always simple, even for very complex road networks. One example of such a model is the multiregression dynamic model (MDM). This thesis focuses on the development of two classes of dynamic graphical models to forecast traffic flows . Firstly, the linear multiregression dynamic model (LMDM), which is an MDM particular case, is extended to allow important traffic characteristics in its structure, such as the heterocedasticity of daily traffic flows, measurement errors due to malfunctions in data collection devices, and the use of extra traffic variables as predictors to forecast flows. Due to its graphical structure, the MDM assumes independence of flows at the entrances of a road network. This thesis therefore introduces a new class of dynamic graphical models where the correlation across road network entrances is accommodated, resulting in better forecasts when compared to the LMDM. All the methodology proposed in this thesis is illustrated using data collected at the intersection of three busy motorways near Manchester, UK.
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Arastuie, Makan. "Generative Models of Link Formation and Community Detection in Continuous-Time Dynamic Networks." University of Toledo / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1596718772873086.
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Fusion, Joe. "The Role of Environmental Dynamics in the Emergence of Autocatalytic Networks." PDXScholar, 2015. https://pdxscholar.library.pdx.edu/open_access_etds/2458.
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For life to arise from non-life, a metabolism must emerge and maintain itself, distinct from its environment. One line of research seeking to understand this emergence has focused on models of autocatalytic reaction networks (ARNs) and the conditions that allow them to approximate metabolic behavior. These models have identified reaction parameters from which a proto-metabolism might emerge given an adequate matter-energy flow through the system. This dissertation extends that research by answering the question: can dynamically structured interactions with the environment promote the emergence of ARNs? This question was inspired by theories that place the origin of life in contexts such as diurnal or tidal cycles. To answer it, an artificial chemistry system with ARN potential was implemented in the dissipative particle dynamics (DPD) modeling paradigm. Unlike differential equation (DE) models favored in prior ARN research, the DPD model is able to simulate environmental dynamics interacting with discrete particles, spatial heterogeneity, and rare events. This dissertation first presents a comparison of the DPD model to published DE results, showing qualitative similarity with some interesting differences. Multiple examples are then provided of dynamically changing flows from the environment that promote emergent ARNs more than constant flows. These include specific cycles of energy and mass flux that consistently increase metrics for ARN concentration and mass focusing. The results also demonstrate interesting nonlinear interactions between the system and cycle amplitude and period. These findings demonstrate the relevance that environmental dynamics has to ARN research and the potential for broader application as well.
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Muller, Lyle. "Spatiotemporal dynamics in neocortex : quantification, analysis, models." Phd thesis, Université Pierre et Marie Curie - Paris VI, 2014. http://tel.archives-ouvertes.fr/tel-01067199.
Full textAbstract:
It has only recently been acknowledged to what large extent the internal dynamics of neural networks could play a role in their function. In this respect, synaptic "noise" -- that is, the influence of the cortical network on single neurons exerted through the massive recurrent circuity that is the hallmark of neocortex -- has recently been shown to have a profound effect on neuronal integrative properties, changing the responses of single neurons across brain states, sometimes within the matter of a few seconds. These internally generated activity states, shaped by and continually shaping the plastic synaptic recurrent connections, then combine with the external inputs to produce a rich repertoire of responses to sensory stimuli in primary cortical regions. In this thesis, we have focused on the {\it spatial} aspect of these internal dynamics, specifically the spatial structure of cortical oscillations, spontaneous and stimulus-evoked. Along the way, we have made an extensive review of the literature concerning propagating waves in thalamus and cortex, and studied network models to investigate how waves depend on network state. We have also introduced new tools for the characterization of spatiotemporal activity patterns in noisy multichannel data. The culmination of this work is a demonstration, using voltage-sensitive dye imaging data taken from the awake monkey, that the population response to a small visual stimulus propagates like a wave across a large extent of primary visual cortex during the awake state, a result contradicting a range of previous studies which seemed to suggest that propagating waves disappear in this case. Moving forward, we have begun to investigate the spatiotemporal structure of local field potential and spiking activity in multielectrode recordings taken from the human and monkey in various states of arousal, to address questions prompted by our initial voltage-sensitive dye imaging study in the monkey. In parallel, we have initiated an analysis of the extent to which neural connectivity can be characterized by the "small-world" effect, the main result of which is that neural graphs may in fact reside outside the small-world regime. The results from these PhD studies thus span the spectrum of scales in neuroscience, from macroscopic activity patterns to microscopic connectivity profiles. It is my sincere hope to expound in these pages a unified theme for these results, and a foundation for further work in neuroscience -- a search for structure within the internal architecture of the system under study.