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Littérature scientifique sur le sujet « Dynamics network »

Auteur : Grafiati

Publié le 9 mars 2023

Mis à jour le 10 mars 2023

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Articles de revues sur le sujet "Dynamics network"

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Levin, Ilya, Mark Korenblit et Vadim Talis. « STUDY OF SOCIAL NETWORKS’ DYNAMICS BY SIMULATION WITHIN THE NODEXL-EXCEL ENVIRONMENT ». Problems of Education in the 21st Century 54, n^o 1 (20 juin 2013) : 125–37. http://dx.doi.org/10.33225/pec/13.54.125.

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The present study is an analysis of the learning activity, which constitutes simulation of networks and studying their functioning and dynamics. The study is based on using network-like learning environments. Such environments allow building computer models of the network graphs. According to the suggested approach, the students construct dynamic computer models of the networks' graphs, thus implementing various algorithms of such networks’ dynamics. The suggested tool for building the models is the software environment comprising network analysis software NodeXL and a standard spreadsheet Excel. The proposed approach enables the students to visualize the network's dynamics. The paper presents specific examples of network models and various algorithms of the network's dynamics, which were developed based on the proposed approach. Key words: learning environments, modelling, social networks.

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MENDES, R. VILELA. « TOOLS FOR NETWORK DYNAMICS ». International Journal of Bifurcation and Chaos 15, n^o 04 (avril 2005) : 1185–213. http://dx.doi.org/10.1142/s0218127405012715.

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Networks have been studied mainly by statistical methods which emphasize their topological structure. Here, one collects some mathematical tools and results which might be useful to study both the dynamics of agents living on the network and the networks themselves as evolving dynamical systems. They include decomposition of differential dynamics, ergodic techniques, estimates of invariant measures, construction of non deterministic automata, logical approaches, etc. A few network examples are discussed as an application of the dynamical tools.

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Sun, Zejun, Jinfang Sheng, Bin Wang, Aman Ullah et FaizaRiaz Khawaja. « Identifying Communities in Dynamic Networks Using Information Dynamics ». Entropy 22, n^o 4 (9 avril 2020) : 425. http://dx.doi.org/10.3390/e22040425.

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Identifying communities in dynamic networks is essential for exploring the latent network structures, understanding network functions, predicting network evolution, and discovering abnormal network events. Many dynamic community detection methods have been proposed from different viewpoints. However, identifying the community structure in dynamic networks is very challenging due to the difficulty of parameter tuning, high time complexity and detection accuracy decreasing as time slices increase. In this paper, we present a dynamic community detection framework based on information dynamics and develop a dynamic community detection algorithm called DCDID (dynamic community detection based on information dynamics), which uses a batch processing technique to incrementally uncover communities in dynamic networks. DCDID employs the information dynamics model to simulate the exchange of information among nodes and aims to improve the efficiency of community detection by filtering out the unchanged subgraph. To illustrate the effectiveness of DCDID, we extensively test it on synthetic and real-world dynamic networks, and the results demonstrate that the DCDID algorithm is superior to the representative methods in relation to the quality of dynamic community detection.

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Liang, Junhao, et Changsong Zhou. « Criticality enhances the multilevel reliability of stimulus responses in cortical neural networks ». PLOS Computational Biology 18, n^o 1 (31 janvier 2022) : e1009848. http://dx.doi.org/10.1371/journal.pcbi.1009848.

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Cortical neural networks exhibit high internal variability in spontaneous dynamic activities and they can robustly and reliably respond to external stimuli with multilevel features–from microscopic irregular spiking of neurons to macroscopic oscillatory local field potential. A comprehensive study integrating these multilevel features in spontaneous and stimulus–evoked dynamics with seemingly distinct mechanisms is still lacking. Here, we study the stimulus–response dynamics of biologically plausible excitation–inhibition (E–I) balanced networks. We confirm that networks around critical synchronous transition states can maintain strong internal variability but are sensitive to external stimuli. In this dynamical region, applying a stimulus to the network can reduce the trial-to-trial variability and shift the network oscillatory frequency while preserving the dynamical criticality. These multilevel features widely observed in different experiments cannot simultaneously occur in non-critical dynamical states. Furthermore, the dynamical mechanisms underlying these multilevel features are revealed using a semi-analytical mean-field theory that derives the macroscopic network field equations from the microscopic neuronal networks, enabling the analysis by nonlinear dynamics theory and linear noise approximation. The generic dynamical principle revealed here contributes to a more integrative understanding of neural systems and brain functions and incorporates multimodal and multilevel experimental observations. The E–I balanced neural network in combination with the effective mean-field theory can serve as a mechanistic modeling framework to study the multilevel neural dynamics underlying neural information and cognitive processes.

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Hu, Ziping, Krishnaiyan Thulasiraman et Pramode K. Verma. « Complex Networks : Traffic Dynamics, Network Performance, and Network Structure ». American Journal of Operations Research 03, n^o 01 (2013) : 187–95. http://dx.doi.org/10.4236/ajor.2013.31a018.

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Hütt, Marc-Thorsten, Marcus Kaiser et Claus C. Hilgetag. « Perspective : network-guided pattern formation of neural dynamics ». Philosophical Transactions of the Royal Society B : Biological Sciences 369, n^o 1653 (5 octobre 2014) : 20130522. http://dx.doi.org/10.1098/rstb.2013.0522.

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The understanding of neural activity patterns is fundamentally linked to an understanding of how the brain's network architecture shapes dynamical processes. Established approaches rely mostly on deviations of a given network from certain classes of random graphs. Hypotheses about the supposed role of prominent topological features (for instance, the roles of modularity, network motifs or hierarchical network organization) are derived from these deviations. An alternative strategy could be to study deviations of network architectures from regular graphs (rings and lattices) and consider the implications of such deviations for self-organized dynamic patterns on the network. Following this strategy, we draw on the theory of spatio-temporal pattern formation and propose a novel perspective for analysing dynamics on networks, by evaluating how the self-organized dynamics are confined by network architecture to a small set of permissible collective states. In particular, we discuss the role of prominent topological features of brain connectivity, such as hubs, modules and hierarchy, in shaping activity patterns. We illustrate the notion of network-guided pattern formation with numerical simulations and outline how it can facilitate the understanding of neural dynamics.

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Galizia, Roberto, et Petri T. Piiroinen. « Regions of Reduced Dynamics in Dynamic Networks ». International Journal of Bifurcation and Chaos 31, n^o 06 (mai 2021) : 2150080. http://dx.doi.org/10.1142/s0218127421500802.

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We consider complex networks where the dynamics of each interacting agent is given by a nonlinear vector field and the connections between the agents are defined according to the topology of undirected simple graphs. The aim of the work is to explore whether the asymptotic dynamic behavior of the entire network can be fully determined from the knowledge of the dynamic properties of the underlying constituent agents. While the complexity that arises by connecting many nonlinear systems hinders us to analytically determine general solutions, we show that there are conditions under which the dynamical properties of the constituent agents are equivalent to the dynamical properties of the entire network. This feature, which depends on the nature and structure of both the agents and connections, leads us to define the concept of regions of reduced dynamics, which are subsets of the parameter space where the asymptotic solutions of a network behave equivalently to the limit sets of the constituent agents. On one hand, we discuss the existence of regions of reduced dynamics, which can be proven in the case of diffusive networks of identical agents with all-to-all topologies and conjectured for other topologies. On the other hand, using three examples, we show how to locate regions of reduced dynamics in parameter space. In simple cases, this can be done analytically through bifurcation analysis and in other cases we exploit numerical continuation methods.

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Chen, Kevin S. « Optimal Population Coding for Dynamic Input by Nonequilibrium Networks ». Entropy 24, n^o 5 (25 avril 2022) : 598. http://dx.doi.org/10.3390/e24050598.

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The efficient coding hypothesis states that neural response should maximize its information about the external input. Theoretical studies focus on optimal response in single neuron and population code in networks with weak pairwise interactions. However, more biological settings with asymmetric connectivity and the encoding for dynamical stimuli have not been well-characterized. Here, we study the collective response in a kinetic Ising model that encodes the dynamic input. We apply gradient-based method and mean-field approximation to reconstruct networks given the neural code that encodes dynamic input patterns. We measure network asymmetry, decoding performance, and entropy production from networks that generate optimal population code. We analyze how stimulus correlation, time scale, and reliability of the network affect optimal encoding networks. Specifically, we find network dynamics altered by statistics of the dynamic input, identify stimulus encoding strategies, and show optimal effective temperature in the asymmetric networks. We further discuss how this approach connects to the Bayesian framework and continuous recurrent neural networks. Together, these results bridge concepts of nonequilibrium physics with the analyses of dynamics and coding in networks.

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Chen, Kevin S. « Optimal Population Coding for Dynamic Input by Nonequilibrium Networks ». Entropy 24, n^o 5 (25 avril 2022) : 598. http://dx.doi.org/10.3390/e24050598.

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The efficient coding hypothesis states that neural response should maximize its information about the external input. Theoretical studies focus on optimal response in single neuron and population code in networks with weak pairwise interactions. However, more biological settings with asymmetric connectivity and the encoding for dynamical stimuli have not been well-characterized. Here, we study the collective response in a kinetic Ising model that encodes the dynamic input. We apply gradient-based method and mean-field approximation to reconstruct networks given the neural code that encodes dynamic input patterns. We measure network asymmetry, decoding performance, and entropy production from networks that generate optimal population code. We analyze how stimulus correlation, time scale, and reliability of the network affect optimal encoding networks. Specifically, we find network dynamics altered by statistics of the dynamic input, identify stimulus encoding strategies, and show optimal effective temperature in the asymmetric networks. We further discuss how this approach connects to the Bayesian framework and continuous recurrent neural networks. Together, these results bridge concepts of nonequilibrium physics with the analyses of dynamics and coding in networks.

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Chen, Kevin S. « Optimal Population Coding for Dynamic Input by Nonequilibrium Networks ». Entropy 24, n^o 5 (25 avril 2022) : 598. http://dx.doi.org/10.3390/e24050598.

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The efficient coding hypothesis states that neural response should maximize its information about the external input. Theoretical studies focus on optimal response in single neuron and population code in networks with weak pairwise interactions. However, more biological settings with asymmetric connectivity and the encoding for dynamical stimuli have not been well-characterized. Here, we study the collective response in a kinetic Ising model that encodes the dynamic input. We apply gradient-based method and mean-field approximation to reconstruct networks given the neural code that encodes dynamic input patterns. We measure network asymmetry, decoding performance, and entropy production from networks that generate optimal population code. We analyze how stimulus correlation, time scale, and reliability of the network affect optimal encoding networks. Specifically, we find network dynamics altered by statistics of the dynamic input, identify stimulus encoding strategies, and show optimal effective temperature in the asymmetric networks. We further discuss how this approach connects to the Bayesian framework and continuous recurrent neural networks. Together, these results bridge concepts of nonequilibrium physics with the analyses of dynamics and coding in networks.

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Thèses sur le sujet "Dynamics network"

1

Holzhauer, Sascha [Verfasser]. « Dynamic Social Networks in Agent-based Modelling : Increasingly Detailed Approaches of Network Initialisation and Network Dynamics / Sascha Holzhauer ». Kassel : Kassel University Press, 2017. http://d-nb.info/1137030445/34.

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Georgieva, Kristina Boyanova. « Boolean network simulation for exploring the dynamics of industrial networks ». Thesis, Lancaster University, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.289295.

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Perera, Pannilage Supun Sachinthaka. « Topological Approach for Modelling the Structure, Dynamics and Robustness of Supply Chain Networks ». Thesis, The University of Sydney, 2018. http://hdl.handle.net/2123/20418.

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Traditionally simple and linear supply chains have, in recent years, evolved towards highly complex networked systems, due to globalisation and product specialisation. Recent application of network models to supply chain systems have revealed the existence of non-trivial and universal topological footprints, which provide important system level insights. This thesis uses topological network models to investigate the structure, dynamics and robustness of supply chain networks (SCNs). Firstly, the common topological characteristics of real-world SCNs are identified, by considering both undirected inter-firm relationship and directed material flow SCNs. Based on this analysis, it is evident that the number of firm-level connections in each SCN follow the power law distribution with power law exponents in the range of 1.5 - 3.5. A fitness-based growth model is then presented to simulate such topologies. The mechanism through which this growth model operates is justified on the basis of risk averse firm behaviour. The second half of this thesis is concerned with the role of SCN topology on the evolution of cooperation and robustness. It is found that the SCN topology, the level of rationality of firms and the relative payoff differences are all essential elements in the evolution of co-operation when strategic inter-firm interactions in an SCN are represented as Prisoner Dilemma games. Finally, a novel methodology to quantify and improve the robustness of material flow SCNs is presented. Here, the specific case of a material flow SCN with multi-sourcing, which is characterised by a tiered structure with directed and weighted links, is considered. An indicative robustness metric is proposed to characterise the robustness of the SCN, considering the degree to which supply chains overlap with each other. Since this model incorporates information beyond the topology of the SCN, it is a useful tool for decision making by the practitioners.

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Renals, Stephen John. « Speech and neural network dynamics ». Thesis, University of Edinburgh, 1990. http://hdl.handle.net/1842/14271.

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This thesis is concerned with two principal issues. Firstly the radial basis functions (RBF) network is introduced and its properties related to other statistical and neural network classifiers. Results from a series of speech recognition experiments, using this network architecture, are reported. These experiments included a continuous speech recognition task with a 571 word lexicon. Secondly, a study of the dynamics of a simple recurrent network model is presented. This study was performed numerically, via a survey of network power spectra and a detailed investigation of the dynamics displayed by a particular network. Word and sentence recognition errors are reported for a continuous speech recognition system using RBF network phoneme modelling with Viterbi smoothing, using either a restricted grammar or no grammar whatsoever. In a cytopathology task domain the best RBF/Viterbi system produced first choice word errors of 6% and sentence errors of 14%, using a grammar of perplexity 6. This compares with word errors of 4% and sentence errors of 8% using the best CSTR hidden Markov model configuration. RBF networks were also used for a static vowel labelling task using hand-segmented vowels excised from continuous speech. Results were not worse than those obtained using statistical classifiers. The second part of this thesis is a computational study of the dynamics of a recurrent neural network model. Two investigations were undertaken. Firstly, a survey of network power spectra was used to map out the temporal activity of this network model (within a four dimensional parameter space) via summary statistics of the network power spectra. Secondly, the dynamics of a particular network were investigated. The dynamics were analysed using bifurcation diagrams, power spectra, the computation of Liapunov exponents and fractal dimensions and the plotting of 2-dimensional attractor projections. Complex dynamical behaviour was observed including Hopf bifurcations, the Ruell-Takens-Newhouse route to chaos with mode-locking at rational winding numbers, the period-doubling route to chaos and the presence of multiple coexisting attractors.

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Johnson, Hope Amy. « Plasticity of cortical network dynamics ». Diss., Restricted to subscribing institutions, 2009. http://proquest.umi.com/pqdweb?did=1835448081&sid=7&Fmt=2&clientId=1564&RQT=309&VName=PQD.

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Battiston, Federico. « The structure and dynamics of multiplex networks ». Thesis, Queen Mary, University of London, 2017. http://qmro.qmul.ac.uk/xmlui/handle/123456789/30631.

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Network science has provided useful answers to research questions in many fields, from biology to social science, from ecology to urban science. The first analyses of networked systems focused on binary networks, where only the topology of the connections were considered. Soon network scientists started considering weighted networks, to represent interactions with different strength, cost, or distance in space and time. Also, connections are not fixed but change over time. This is why in more recent years, a lot of attention has been devoted to temporal or time-varying networks. We now entered the era of multi-layer networks, or multiplex networks, relational systems whose units are connected by different relationships, with links of distinct types embedded in different layers. Multiplexity has been observed in many contexts, from social network analysis to economics, medicine and ecology. The new challenge consists in applying the new tools of multiplex theory to unveil the richness associated to this novel level of complexity. How do agents organise their interactions across layers? How does this affect the dynamics of the system? In the first part of the thesis, we provide a mathematical framework to deal with multiplex networks. We suggest metrics to unveil multiplexity from basic node, layer and edge properties to more complicated structure at the micro- and meso-scale, such as motifs, communities and cores. Measures are validated through the analysis of real-world systems such as social and collaboration networks, transportation systems and the human brain. In the second part of the thesis we focus on dynamical processes taking place on top of multiplex networks, namely biased random walks, opinion dynamics, cultural dynamics and evolutionary game theory. All these examples show how multiplexity is crucial to determine the emergence of unexpected and instrinsically multiplex collective behavior, opening novel perspectives for the field of non-linear dynamics on networks.

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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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Brookes, Richard. « Structure and dynamics in network liquids ». Thesis, University of Oxford, 2002. http://ora.ox.ac.uk/objects/uuid:af233937-3168-498a-b7cc-eed758f5e5de.

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The onset of the Glass Transition in tetrahedral network liquids is associated with the over-constrained nature of the structure and the low ability of the ions to move relative to one another. We investigate the interplay between the structure and dynamics in BeF₂, a template for the ideal tetrahedral system. We see that the ionic diffusion coefficients can be predicted from the calculated viscosity of the system using the Eyring hopping model of diffusion, with a diffusive jump length approximately corresponding to the radius of the first coordination sphere. Novel correlation functions are developed which enable us to identify the events responsible, on an atomistic level, for the structural rearrangements which correspond to the barrier crossing in this hopping model of diffusion, and we find that these events can be identified as the exchange of ions in the local coordination poly- hedra, or cage, of the cations. The calculation of the rate of the decay of these cages allows us to predict the macroscopic diffusion coefficients with the definition of a jump length over which the diffusive hops occur, and to scale the behaviour of the system at different temperatures by setting the cage lifetime as an effective clock for the system. Comparison between simulations performed with and without the inclusion of the effects of anion polarisation suggest that the polarisation plays an important role in the ability of the system to undergo the cage decay events and to create the defect sites which facilitate a decrease in the number of constraints acting in the system. The decay of the cages describes the local rearrangement of the ions in the first coordi- nation shell of a given ion. The development of other correlation functions allows us to investigate the spatial relationship between these cage decay events over longer length and time scales, and also to investigate how the local structure of the first coordination shells of the cations relates to their ability to undergo the cage decay events and to form the defects. These functions are then used to investigate the link between the structure and the dy- namics in some molten trichloride systems, which have different network structures, and hence a different relationship between the cage decay and the diffusion. Finally, we investigate the effect of changing the potential parameters in BeF2, and we find that the effective polarisability of the system can be controlled such that a less diffusive system may be described, giving a good representation of both structural and dynamical experimental data.

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Ratanachote, Po-ngarm. « Distribution network dynamics with correlated demands ». Thesis, Cardiff University, 2011. http://orca.cf.ac.uk/54428/.

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The distribution network designs for two-level supply chains have been analysed using stochastic analytical methods. The market demands faced by multiple retailers are correlated. The correlated demand is modelled as a first order Vector Auto-Regressive process, which is used to represent the progression of and relationships in sets of time series of demand. All participants are assumed to operate an Order-Up-To policy with a Minimum Mean Squared Error forecasting. Inventory and capacity costs have been considered. Control engineering methods have been exploited to obtain the closed form expressions of the variances of the inventory levels and the order rates. The ratios of costs between the decentralised and centralised systems have been used to evaluate the economic performance of the consolidated distribution network. The variance expressions are the key components for the cost ratios. Insights about the system can also be obtained from the analysis of the variance expressions. The impacts of demand patterns, lead-times and the number of decentralised locations on the consolidation decision have been investigated. The results show that the auto-correlation and cross-correlation of the market demands highly affect the consolidation decisions. The Square Root Law for Inventory and Bullwhip has been proved to hold with certain demand correlations. Consolidation scenarios that are always attractive under a specific demand pattern and a set of constraints about the lead-times have been presented. The structural transition of the demand into orders placed onto higher echelons has been investigated. The result shows that higher echelons may not need the point-of-sales data as it is already contained in the order they receive from the retailers. Finally, the model has been validated by its application to real world data and has shown to be a useful tool for practitioners to investigate the dynamic behaviour and economic performance of the distribution network design.

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Lospinoso, Joshua Alfred. « Statistical models for social network dynamics ». Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:d5ed9b9c-020c-4379-a5f2-cf96439ca37c.

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The study of social network dynamics has become an increasingly important component of many disciplines in the social sciences. In the past decade, statistical models and methods have been proposed which permit researchers to draw statistical inference on these dynamics. This thesis builds on one such family of models, the stochastic actor oriented model (SAOM) proposed by Snijders [2001]. Goodness of fit for SAOMs is an area that is only just beginning to be filled in with appropriate methods. This thesis proposes a Mahalanobis distance based, Monte Carlo goodness of fit test that can depend on arbitrary features of the observed network data and covariates. As remediating poor fit can be a difficult process, a modified model distance (MMD) estimator is devised that can help researchers to choose among a set of model elaborations. In practice, panel data is typically used to draw SAOM-based inference. This thesis also proposes a score-type test for time heterogeneity between the waves in the panel that is computationally cheap and fits into a convenient, forward model selecting workflow. Next, this thesis proposes a rigorous method for aggregating so-called relational event data (e.g. emails and phone calls) by extending the SAOM family to a family of hidden Markov models that suppose a latent social network is driving the observed relational events. Finally, this thesis proposes a measurement model for SAOMs inspired by error-in-variables (EiV) models employed in an array of disciplines. Like the relational event aggregation model, the measurement model is a hidden Markov model extension to the SAOM family. These models allow the researcher to specify the form of the mesurement error and buffer against potential attenuating biases and other problems that can arise if the errors are ignored.

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Livres sur le sujet "Dynamics network"

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Taylor, J. G., E. R. Caianiello, R. M. J. Cotterill et J. W. Clark, dir. Neural Network Dynamics. London : Springer London, 1992. http://dx.doi.org/10.1007/978-1-4471-2001-8.

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Peter, Naudé, et Turnbull Peter W, dir. Network dynamics in international marketing. Oxford : Pergamon, 1998.

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3

Dolgui, Alexandre, Dmitry Ivanov et Boris Sokolov, dir. Supply Network Dynamics and Control. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09179-7.

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4

Menache, Ishai. Network games : Theory, models, and dynamics. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool, 2011.

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W, Sandberg I., dir. Nonlinear dynamical systems : Feedforward neural network perspectives. New York : John Wiley, 2001.

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6

Abergel, Frédéric, Bikas K. Chakrabarti, Anirban Chakraborti et Asim Ghosh, dir. Econophysics of Systemic Risk and Network Dynamics. Milano : Springer Milan, 2013. http://dx.doi.org/10.1007/978-88-470-2553-0.

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A, Dyker David, dir. Network dynamics in emerging regions of Europe. New Jersey : World Scientific, 2010.

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A, Dyker David, dir. Network dynamics in emerging regions of Europe. New Jersey : World Scientific, 2010.

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9

Grewal, David Singh. Network power : The social dynamics of globalization. New Haven : Yale University Press, 2008.

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Grewal, David Singh. Network power : The social dynamics of globalization. New Haven : Yale University Press, 2008.

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Chapitres de livres sur le sujet "Dynamics network"

1

Sauro, Herbert M. « Network Dynamics ». Dans Methods in Molecular Biology, 269–309. Totowa, NJ : Humana Press, 2009. http://dx.doi.org/10.1007/978-1-59745-243-4_13.

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Palm, Günther, et Friedrich T. Sommer. « Information and pattern capacities in neural associative memories with feedback for sparse memory patterns ». Dans Neural Network Dynamics, 3–18. London : Springer London, 1992. http://dx.doi.org/10.1007/978-1-4471-2001-8_1.

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Arndt, M., P. Dicke, M. Erb, R. Eckhorn et H. J. Reitboeck. « Two-Layered Physiology-Oriented Neuronal Network Models that Combine Dynamic Feature Linking via Synchronization with a Classical Associative Memory ». Dans Neural Network Dynamics, 140–54. London : Springer London, 1992. http://dx.doi.org/10.1007/978-1-4471-2001-8_10.

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Holden, A. V., J. V. Tucker, B. C. Thompson, D. Withington et H. Zhang. « Theoretical framework for analysing the behaviour of real and simulated neural networks ». Dans Neural Network Dynamics, 155–69. London : Springer London, 1992. http://dx.doi.org/10.1007/978-1-4471-2001-8_11.

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Hyde, Julie. « Coupled Neuronal Oscillatory Systems ». Dans Neural Network Dynamics, 170–79. London : Springer London, 1992. http://dx.doi.org/10.1007/978-1-4471-2001-8_12.

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Cotterill, Rodney M. J., et Claus Nielsen. « Gamma-Band Oscillations in a Cortical Model ». Dans Neural Network Dynamics, 180–90. London : Springer London, 1992. http://dx.doi.org/10.1007/978-1-4471-2001-8_13.

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Baird, Bill. « Information Processing by Dynamical Interaction of Oscillatory Modes in Coupled Cortical Networks ». Dans Neural Network Dynamics, 191–207. London : Springer London, 1992. http://dx.doi.org/10.1007/978-1-4471-2001-8_14.

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Borisyuk, Galina N., Roman M. Borisyuk et Alexander I. Khibnik. « Analysis of Oscillatory Regimes of a Coupled Neural Oscillator System with Application to Visual Cortex Modeling ». Dans Neural Network Dynamics, 208–25. London : Springer London, 1992. http://dx.doi.org/10.1007/978-1-4471-2001-8_15.

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Niebur, Ernst, Heinz G. Schuster et Daniel M. Kammen. « Systems of Relaxation Oscillators with Time-Delayed Coupling ». Dans Neural Network Dynamics, 226–33. London : Springer London, 1992. http://dx.doi.org/10.1007/978-1-4471-2001-8_16.

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Destexhe, A., et A. Babloyantz. « Cortical Coherent Activity Induced by Thalamic Oscillations ». Dans Neural Network Dynamics, 234–49. London : Springer London, 1992. http://dx.doi.org/10.1007/978-1-4471-2001-8_17.

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Actes de conférences sur le sujet "Dynamics network"

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Yang, Chun-Lin, et C. Steve Suh. « On the Dynamics of Complex Network ». Dans ASME 2017 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/imece2017-71994.

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Controlling complex network systems is challenging because network systems are highly coupled by ensembles and behaving with uncertainty. A network is composed by nodes and edges. Edges serve as the connection between nodes to exchange state information and further achieve state consensus. Through edges, the dynamics of individual nodes at the local level intimately affects the network dynamics at the global level. As a following bird can occasionally lose visual contact with the target bird in a flock at any moment, the edge between two nodes in a real world network systems is not necessarily always intact. Contrary to common sense, these real-world networks are usually perfectly stable even when the edges between the nodes are unstable. This suggests that not only nodes are dynamical, edges are dynamical, too. Since the edges between the nodes are changing dynamically, network configuration is also dynamical. Further, edges need be defined and quantified so that the unstable connection behavior can be properly described. The paper explores the concepts of statistical mechanics and statistical entropy to address the particular need. Statistical mechanics describes the behavior of a mechanical system that has uncertain states. Statistical entropy on the other hand defines the distribution of the microstates by probability. Entropy provides a measure of the level of network integrity. With entropy, one can assign desired dynamics to the network to ensure desired network property. This work aims to construct a complex network structure model based on the edge dynamics. Coupled with node self-dynamic and consensus law, a general dynamical network model can be constructed.

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Yang, Chun-Lin, et C. Steve Suh. « On the Proper Description of Complex Network Dynamics ». Dans ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-88051.

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Real-world networks are dynamical complex network systems. The dynamics of a network system is a coupling of the local dynamics with the global dynamics. The local dynamics is the time-varying behaviors of ensembles at the local level. The global dynamics is the collective behavior of the ensembles following specific laws at the global level. These laws include basic physical principles and constraints. Complex networks have inherent resilience that offsets disturbance and maintains the state of the system. However, when disturbance is potent enough, network dynamics can be perturbed to a level that ensembles no longer follow the constraint conditions. As a result, the collective behavior of a complex network diminishes and the network collapses. The characteristic of a complex network is the response of the system which is time-dependent. Therefore, complex networks need to account for time-dependency and obey physical laws and constraints. Statistical mechanics is viable for the study of multi-body dynamic systems having uncertain states such as complex network systems. Statistical entropy can be used to define the distribution of the states of ensembles. The difference between the states of ensembles define the interaction between them. This interaction is known as the collective behavior. In other words statistical entropy defines the dynamics of a complex network. Variation of entropy corresponds to the variation of network dynamics and vice versa. Therefore, entropy can serve as an indicator of network dynamics. A stable network is characterized by a specific entropy while a network on the verge of collapse is characterized by another. As the collective behavior of a complex network can be described by entropy, the correlation between the statistical entropy and network dynamics is investigated.

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Katasev, Alexey S., et Dina V. Kataseva. « Neural network diagnosis of anomalous network activity in telecommunication systems ». Dans 2016 Dynamics of Systems, Mechanisms and Machines (Dynamics). IEEE, 2016. http://dx.doi.org/10.1109/dynamics.2016.7819020.

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Yang, Chun-Lin, Nandan Shettigar et C. Steve Suh. « A Proposition for Describing Real-World Network Dynamics ». Dans ASME 2021 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/imece2021-73360.

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Abstract This study presents a proposition for describing the dynamics of real-world networks under the general framework of complex networks. Outward behaviors of complex networks are the manifestation of the coupled dynamics at the macroscopic level and the individual dynamics at the microscopic level. At the macroscopic level a law of coupling governs the interactions of network constituents. At the microscopic level, the dynamics of individual constituent is defined by energy that follows normal distribution. Constituent dynamics are bounded by physical constraints. Consequently, network dynamics can be quantified using information entropy which is a function of constituent energy. In real-world networks, differences between individual constituents exist due to differing mechanical properties and dynamics. Consequently, network dynamics are of different layers and hierarchies. Construct of network governing equations formulated under the general framework of complex networks are demonstrated using two real-world networks — a brain network and a lymph node network. Brain network is constructed by the neurons that each connected by the synapse. Brain network dynamics is composed by the law of coupling defined by the synaptic dynamics through the transmitting of neurotransmitters that couples the individual neuron dynamics. Since different classifications exist among neurotransmitters and neurons, the post synaptic neuron can present either inhibitory or excitatory action. The inhibitory and excitatory behavior of the neurons changes the mechanical properties of each neuron and further alters the brain network dynamics. Consequently, the brain network emerges dynamics with different layers. Lymph node network drains fluid from blood vessels, filter the lymph (the interstitial fluid lymphatic system collects from the blood circulation) through lymph nodes, and transport the lymph back to the blood circulation. Lymph node dynamics is composed by the dynamics of lymph transportation along the lymph node network and the individual lymph node dynamics that involves lymphocytes-pathogens interactions (adaptive immune response). In each lymph node, lymphocytes fight off the pathogens which also emerges a network dynamics such as the interaction between T cells and HIV viruses. Finally, the lymph is collected from each lymph nodes and drained back to the blood circulation. As a result, the lymph node network has the dynamics of different hierarchies where the lymphocytes-pathogens dynamics exists within each lymph node at the lower hierarchy is further under the influence of the lymph transportation dynamics among the whole lymph node network on the higher hierarchy. Since the constituent dynamics of the brain network and lymph node network can be defined by energy that follows normal distribution and both are bounded by physical constraints, the network dynamics of both cases can be quantified through information entropy. Features pertaining to the global as well as individual constituent dynamics of the networks are identified that are insightful to the control of such complex networks.

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Zabudsky, Gennady, et Maria Lisina. « Approximately Algorithm for Maximin Location Problem on Network ». Dans 2018 Dynamics of Systems, Mechanisms and Machines (Dynamics). IEEE, 2018. http://dx.doi.org/10.1109/dynamics.2018.8601502.

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Semakhin, Andrei M. « Network Simulation of Information System in Conditions Of Uncertainty ». Dans 2018 Dynamics of Systems, Mechanisms and Machines (Dynamics). IEEE, 2018. http://dx.doi.org/10.1109/dynamics.2018.8601500.

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Baras, John, al e, Mike Ball, Ramesh Karne, Steve Kelley, Kap Jang, Catherine Plaisant et al. « Hybrid network management ». Dans Dynamics Specialists Conference. Reston, Virigina : American Institute of Aeronautics and Astronautics, 1996. http://dx.doi.org/10.2514/6.1996-1185.

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Anikin, Igor V. « Information security risks assessment in telecommunication network of the university ». Dans 2016 Dynamics of Systems, Mechanisms and Machines (Dynamics). IEEE, 2016. http://dx.doi.org/10.1109/dynamics.2016.7818967.

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Monakhov, Yuri M., Mikhail Yu Monakhov et Andrey V. Telny. « Improving the Accuracy of Navigation Measurements of Mobile Network Nodes ». Dans 2018 Dynamics of Systems, Mechanisms and Machines (Dynamics). IEEE, 2018. http://dx.doi.org/10.1109/dynamics.2018.8601427.

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Zhao, Guofeng, Dan Li, Chuan Xu, Hong Tang et Shui Yu. « Network dynamics of mobile social networks ». Dans ICC 2014 - 2014 IEEE International Conference on Communications. IEEE, 2014. http://dx.doi.org/10.1109/icc.2014.6883695.

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Rapports d'organisations sur le sujet "Dynamics network"

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Pineda, Fernando J. Investigation of Neural Network Dynamics. Fort Belvoir, VA : Defense Technical Information Center, août 1989. http://dx.doi.org/10.21236/ada216791.

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Paganini, Fernando. Content Dynamics Over the Network Cloud. Fort Belvoir, VA : Defense Technical Information Center, novembre 2015. http://dx.doi.org/10.21236/ada627413.

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Xu, Chonggang, Youzuo Lin, Nishant Panda, Monty Vesselinov et Humberto Vazquez. Process-based Neural Network to Forecast Vegetation Dynamics. Office of Scientific and Technical Information (OSTI), avril 2021. http://dx.doi.org/10.2172/1769768.

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Wilson, Charles L., James L. Blue et Omid M. Omidvar. Improving neural network performance for character and fingerprint classification by altering network dynamics. Gaithersburg, MD : National Institute of Standards and Technology, 1995. http://dx.doi.org/10.6028/nist.ir.5695.

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Macker, Joseph P., et Vincent D. Park. Heterogeneous Architecture Support for Wireless Network Dynamics and Mobility. Fort Belvoir, VA : Defense Technical Information Center, décembre 2000. http://dx.doi.org/10.21236/ada389245.

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Wilson, Charles L., James L. Blue et Omid M. Omidvar. The effect of training dynamics on neural network performance. Gaithersburg, MD : National Institute of Standards and Technology, 1995. http://dx.doi.org/10.6028/nist.ir.5696.

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Korzeniowski, Andrzej, et Gangaram S. Ladde. Modeling of Network Dynamics under Markovian and Structural Perturbations. Fort Belvoir, VA : Defense Technical Information Center, mars 2011. http://dx.doi.org/10.21236/ada545400.

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Yeates, Jessica. The Foundations of Network Dynamics in an RNA Recombinase System. Portland State University Library, janvier 2000. http://dx.doi.org/10.15760/etd.2915.

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Lee, Kyu-Hye, et Song-yi Youn. Global Market Dynamics of Korean Cosmetics : Network Analysis of International Trade. Ames (Iowa) : Iowa State University. Library, janvier 2019. http://dx.doi.org/10.31274/itaa.8819.

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Letchford, Joshua, et Ruby Booth. Gaming Research for Alliance Network Dynamics ? Report 2 -- Assent Draft Ruleset. Office of Scientific and Technical Information (OSTI), février 2021. http://dx.doi.org/10.2172/1765623.

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