Bibliographies thématiques / Networks dynamic

Littérature scientifique sur le sujet « Networks dynamic »

Auteur : Grafiati
Publié le 25 mai 2024

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Articles de revues sur le sujet "Networks dynamic"

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CHIU, CHINCHUAN, et MICHAEL A. SHANBLATT. « HUMAN-LIKE DYNAMIC PROGRAMMING NEURAL NETWORKS FOR DYNAMIC TIME WARPING SPEECH RECOGNITION ». International Journal of Neural Systems 06, no 01 (mars 1995) : 79–89. http://dx.doi.org/10.1142/s012906579500007x.

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This paper presents a human-like dynamic programming neural network method for speech recognition using dynamic time warping. The networks are configured, much like human’s, such that the minimum states of the network’s energy function represent the near-best correlation between test and reference patterns. The dynamics and properties of the neural networks are analytically explained. Simulations for classifying speaker-dependent isolated words, consisting of 0 to 9 and A to Z, show that the method is better than conventional methods. The hardware implementation of this method is also presented.
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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, no 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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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, no 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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Melamed, David, Ashley Harrell et Brent Simpson. « Cooperation, clustering, and assortative mixing in dynamic networks ». Proceedings of the National Academy of Sciences 115, no 5 (16 janvier 2018) : 951–56. http://dx.doi.org/10.1073/pnas.1715357115.

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Humans’ propensity to cooperate is driven by our embeddedness in social networks. A key mechanism through which networks promote cooperation is clustering. Within clusters, conditional cooperators are insulated from exploitation by noncooperators, allowing them to reap the benefits of cooperation. Dynamic networks, where ties can be shed and new ties formed, allow for the endogenous emergence of clusters of cooperators. Although past work suggests that either reputation processes or network dynamics can increase clustering and cooperation, existing work on network dynamics conflates reputations and dynamics. Here we report results from a large-scale experiment (total n = 2,675) that embedded participants in clustered or random networks that were static or dynamic, with varying levels of reputational information. Results show that initial network clustering predicts cooperation in static networks, but not in dynamic ones. Further, our experiment shows that while reputations are important for partner choice, cooperation levels are driven purely by dynamics. Supplemental conditions confirmed this lack of a reputation effect. Importantly, we find that when participants make individual choices to cooperate or defect with each partner, as opposed to a single decision that applies to all partners (as is standard in the literature on cooperation in networks), cooperation rates in static networks are as high as cooperation rates in dynamic networks. This finding highlights the importance of structured relations for sustained cooperation, and shows how giving experimental participants more realistic choices has important consequences for whether dynamic networks promote higher levels of cooperation than static networks.
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Nie, Chun-Xiao. « Hurst analysis of dynamic networks ». Chaos : An Interdisciplinary Journal of Nonlinear Science 32, no 2 (février 2022) : 023130. http://dx.doi.org/10.1063/5.0070170.

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The sequence of network snapshots with time stamps is an effective tool for describing system dynamics. First, this article constructs a multifractal analysis of a snapshot network, in which the Hurst integral is used to describe the fractal structure hidden in structural dynamics. Second, we adjusted the network model and conducted comparative analysis to clarify the meaning of the Hurst exponent and found that the snapshot network usually includes multiple fractal structures, such as local and global fractal structures. Finally, we discussed the fractal structure of two real network datasets. We found that the real snapshot network also includes rich dynamics, which can be distinguished by the Hurst exponent. In particular, the dynamics of financial networks includes multifractal structures. This article provides a perspective to study the dynamic networks, thereby indirectly describing the fractal characteristics of complex system dynamics.
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Iedema, Rick, Raj Verma, Sonia Wutzke, Nigel Lyons et Brian McCaughan. « A network of networks ». Journal of Health Organization and Management 31, no 2 (10 avril 2017) : 223–36. http://dx.doi.org/10.1108/jhom-07-2016-0146.

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Purpose To further our insight into the role of networks in health system reform, the purpose of this paper is to investigate how one agency, the NSW Agency for Clinical Innovation (ACI), and the multiple networks and enabling resources that it encompasses, govern, manage and extend the potential of networks for healthcare practice improvement. Design/methodology/approach This is a case study investigation which took place over ten months through the first author’s participation in network activities and discussions with the agency’s staff about their main objectives, challenges and achievements, and with selected services around the state of New South Wales to understand the agency’s implementation and large system transformation activities. Findings The paper demonstrates that ACI accommodates multiple networks whose oversight structures, self-organisation and systems change approaches combined in dynamic ways, effectively yield a diversity of network governances. Further, ACI bears out a paradox of “centralised decentralisation”, co-locating agents of innovation with networks of implementation and evaluation expertise. This arrangement strengthens and legitimates the role of the strategic hybrid – the healthcare professional in pursuit of change and improvement, and enhances their influence and impact on the wider system. Research limitations/implications While focussing the case study on one agency only, this study is unique as it highlights inter-network connections. Contributing to the literature on network governance, this paper identifies ACI as a “network of networks” through which resources, expectations and stakeholder dynamics are dynamically and flexibly mediated and enhanced. Practical implications The co-location of and dynamic interaction among clinical networks may create synergies among networks, nurture “strategic hybrids”, and enhance the impact of network activities on health system reform. Social implications Network governance requires more from network members than participation in a single network, as it involves health service professionals and consumers in a multi-network dynamic. This dynamic requires deliberations and collaborations to be flexible, and it increasingly positions members as “strategic hybrids” – people who have moved on from singular taken-as-given stances and identities, towards hybrid positionings and flexible perspectives. Originality/value This paper is novel in that it identifies a critical feature of health service reform and large system transformation: network governance is empowered through the dynamic co-location of and collaboration among healthcare networks, particularly when complemented with “enabler” teams of people specialising in programme implementation and evaluation.
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Galizia, Roberto, et Petri T. Piiroinen. « Regions of Reduced Dynamics in Dynamic Networks ». International Journal of Bifurcation and Chaos 31, no 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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Wu, Wei, et Xuemeng Zhai. « DyLFG : A Dynamic Network Learning Framework Based on Geometry ». Entropy 25, no 12 (30 novembre 2023) : 1611. http://dx.doi.org/10.3390/e25121611.

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Dynamic network representation learning has recently attracted increasing attention because real-world networks evolve over time, that is nodes and edges join or leave the networks over time. Different from static networks, the representation learning of dynamic networks should not only consider how to capture the structural information of network snapshots, but also consider how to capture the temporal dynamic information of network structure evolution from the network snapshot sequence. From the existing work on dynamic network representation, there are two main problems: (1) A significant number of methods target dynamic networks, which only allow nodes to increase over time, not decrease, which reduces the applicability of such methods to real-world networks. (2) At present, most network-embedding methods, especially dynamic network representation learning approaches, use Euclidean embedding space. However, the network itself is geometrically non-Euclidean, which leads to geometric inconsistencies between the embedded space and the underlying space of the network, which can affect the performance of the model. In order to solve the above two problems, we propose a geometry-based dynamic network learning framework, namely DyLFG. Our proposed framework targets dynamic networks, which allow nodes and edges to join or exit the network over time. In order to extract the structural information of network snapshots, we designed a new hyperbolic geometry processing layer, which is different from the previous literature. In order to deal with the temporal dynamics of the network snapshot sequence, we propose a gated recurrent unit (GRU) module based on Ricci curvature, that is the RGRU. In the proposed framework, we used a temporal attention layer and the RGRU to evolve the neural network weight matrix to capture temporal dynamics in the network snapshot sequence. The experimental results showed that our model outperformed the baseline approaches on the baseline datasets.
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Chen, Kevin S. « Optimal Population Coding for Dynamic Input by Nonequilibrium Networks ». Entropy 24, no 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, no 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 "Networks dynamic"

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Horsch, Michael C. « Dynamic Bayesian networks ». Thesis, University of British Columbia, 1990. http://hdl.handle.net/2429/28909.

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Given the complexity of the domains for which we would like to use computers as reasoning engines, an automated reasoning process will often be required to perform under some state of uncertainty. Probability provides a normative theory with which uncertainty can be modelled. Without assumptions of independence from the domain, naive computations of probability are intractible. If probability theory is to be used effectively in AI applications, the independence assumptions from the domain should be represented explicitly, and used to greatest possible advantage. One such representation is a class of mathematical structures called Bayesian networks. This thesis presents a framework for dynamically constructing and evaluating Bayesian networks. In particular, this thesis investigates the issue of representing probabilistic knowledge which has been abstracted from particular individuals to which this knowledge may apply, resulting in a simple representation language. This language makes the independence assumptions for a domain explicit. A simple procedure is provided for building networks from knowledge expressed in this language. The mapping between the knowledge base and network created is precisely defined, so that the network always represents a consistent probability distribution. Finally, this thesis investigates the issue of modifying the network after some evaluation has taken place, and several techniques for correcting the state of the resulting model are derived.
Science, Faculty of
Computer Science, Department of
Graduate
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Fard, Pedram J. « Dynamic reconfiguration of network topology in optical networks ». College Park, Md. : University of Maryland, 2007. http://hdl.handle.net/1903/7412.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2007.
Thesis research directed by: Electrical Engineering. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Robinson, Anthony John. « Dynamic error propagation networks ». Thesis, University of Cambridge, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.303145.

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Al-Dujaily, Ra'ed. « Embedded dynamic programming networks for networks-on-chip ». Thesis, University of Newcastle upon Tyne, 2013. http://hdl.handle.net/10443/1884.

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Relentless technology downscaling and recent technological advancements in three dimensional integrated circuit (3D-IC) provide a promising prospect to realize heterogeneous system-on-chip (SoC) and homogeneous chip multiprocessor (CMP) based on the networks-onchip (NoCs) paradigm with augmented scalability, modularity and performance. In many cases in such systems, scheduling and managing communication resources are the major design and implementation challenges instead of the computing resources. Past research efforts were mainly focused on complex design-time or simple heuristic run-time approaches to deal with the on-chip network resource management with only local or partial information about the network. This could yield poor communication resource utilizations and amortize the benefits of the emerging technologies and design methods. Thus, the provision for efficient run-time resource management in large-scale on-chip systems becomes critical. This thesis proposes a design methodology for a novel run-time resource management infrastructure that can be realized efficiently using a distributed architecture, which closely couples with the distributed NoC infrastructure. The proposed infrastructure exploits the global information and status of the network to optimize and manage the on-chip communication resources at run-time. There are four major contributions in this thesis. First, it presents a novel deadlock detection method that utilizes run-time transitive closure (TC) computation to discover the existence of deadlock-equivalence sets, which imply loops of requests in NoCs. This detection scheme, TC-network, guarantees the discovery of all true-deadlocks without false alarms in contrast to state-of-the-art approximation and heuristic approaches. Second, it investigates the advantages of implementing future on-chip systems using three dimensional (3D) integration and presents the design, fabrication and testing results of a TC-network implemented in a fully stacked three-layer 3D architecture using a through-silicon via (TSV) complementary metal-oxide semiconductor (CMOS) technology. Testing results demonstrate the effectiveness of such a TC-network for deadlock detection with minimal computational delay in a large-scale network. Third, it introduces an adaptive strategy to effectively diffuse heat throughout the three dimensional network-on-chip (3D-NoC) geometry. This strategy employs a dynamic programming technique to select and optimize the direction of data manoeuvre in NoC. It leads to a tool, which is based on the accurate HotSpot thermal model and SystemC cycle accurate model, to simulate the thermal system and evaluate the proposed approach. Fourth, it presents a new dynamic programming-based run-time thermal management (DPRTM) system, including reactive and proactive schemes, to effectively diffuse heat throughout NoC-based CMPs by routing packets through the coolest paths, when the temperature does not exceed chip’s thermal limit. When the thermal limit is exceeded, throttling is employed to mitigate heat in the chip and DPRTM changes its course to avoid throttled paths and to minimize the impact of throttling on chip performance. This thesis enables a new avenue to explore a novel run-time resource management infrastructure for NoCs, in which new methodologies and concepts are proposed to enhance the on-chip networks for future large-scale 3D integration.
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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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Ho, Koki. « Dynamic network modeling for spaceflight logistics with time-expanded networks ». Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/98557.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2015.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 139-145).
This research develops a dynamic logistics network formulation for high-level lifecycle optimization of space mission sequences in order to find an optimal space transportation architecture considering its technology trades over time. The proposed methodology is inspired by terrestrial logistics analysis techniques based on linear programming network optimization. A new model with a generalized multi-commodity network flow formulation and a time-expanded network is developed for dynamic space logistics optimization. The developed methodology is applied to three case studies: 1) human exploration of Mars; 2) human exploration of a near-Earth object (NEO); 3) their combination (related to the concept of the Flexible Path). The results reveal multiple dynamic system-level trades over time and provide recommendations for an optimal strategy for human space exploration architecture. The considered trades include those between in-situ resource utilization (ISRU) and propulsion technologies as well as orbit and depot location selection over time. The numerical results show that using specific combinations of propulsion technologies, ISRU, and other space infrastructure elements effectively, we can reduce the initial mass in low- Earth orbit (IMLEO) by 45-50% compared with the baseline architecture. In addition, the analysis results also show that we can achieve 15-20% IMLEO reduction by designing Mars and NEO missions together as a campaign compared with designing them separately owing to their common space logistics infrastructure pre-deployment. This research serves as a precursor for eventual permanent settlement and colonization of other planets by humans, thus transforming us into a multi-planet species.
by Koki Ho.
Ph. D.
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Bienkowski, Marcin. « Page migration in dynamic networks ». [S.l. : s.n.], 2005. http://deposit.ddb.de/cgi-bin/dokserv?idn=976779188.

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May, Alex. « Tensor networks for dynamic spacetimes ». Thesis, University of British Columbia, 2017. http://hdl.handle.net/2429/62730.

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Tensor networks give simple representations of complex quantum states. They have proven useful in the study of condensed matter systems and conformal fields, and recently have provided toy models of AdS/CFT. Underlying the tensor network - AdS/CFT connection is the association of a graph geometry with the tensor network. This geometry is most easily understood as containing only spatial directions. In the context of the AdS/CFT correspondence this limits tensor network toy models to describing static spacetimes. Here we look to extend tensor network models of AdS/CFT by capturing the geometry of a dynamic spacetime in a network description. We review the role of tensor networks in our understanding of AdS/CFT to motivate this extension, before proposing a network picture that captures key features of AdS/CFT.
Science, Faculty of
Physics and Astronomy, Department of
Graduate
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Lesiuk, Bryan Cameron. « Dynamic routing for measurement networks ». Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/MQ62556.pdf.

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Afsariardchi, Niloufar. « Community detection in dynamic networks ». Thesis, McGill University, 2013. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=114565.

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A reasonable representation of some complex systems such as social and biological systems is a network topology that allows its components and interactions among them to change over time. Understanding the time-dependence of these networks can lead to invaluable insight about characteristics and structure of time-varying networks. In this thesis, several classes of static and dynamic clustering algorithms and ideas are reviewed. A challenge arising in dynamic clustering schemes is that the detected communities are not independent over time and the identified clusters at one point of time should not dramatically deviate from the results of previous timesteps. It is especially important to reduce large short term variations and ensure that communities smoothly change over time. Here we present a novel method which is built upon a probabilistic generative Bayesian model to address the problem of identifying consistent and stable overlapping communities in dynamic networks. Synthetic and real networks are used to evaluate the performance with respect to different parameter settings, the model order selection, and the run-time of the proposed algorithm. Performance analysis indicates thatthe algorithm proposed in this thesis outperforms several other state-of-the-art algorithms and provides valuable insights into the evolution and underlying structure.
Une représentation raisonnable de certains systèmes complexes tels que les systèmes sociaux et biologiques est une topologie de réseau qui permet à ses composants et les interactions entre eux de changer au fil du temps. Comprendre la dépendance temporelle de ces réseaux, conduire à de précieux renseignements sur les caractéristiques et la structure de variables dans le temps des réseaux. Dans cette thèse, plusieurs classes d'algorithmes de clustering statiques et dynamiques et des idées sont passées en revue. Un défi se pose dans des plans de regroupement dynamiques est que les communautés détectées ne sont pas indépendants dans le temps et les grappes fondées à un moment donné du temps ne doit pas s'écarter de façon spectaculaire à partir des résultats de pas de temps précédents. Spécialement, il est de l'importance de diminuer de fortes variations à court terme et d'assurer que les communautés progressivement changer au fil du temps. Ici, nous présentons une nouvelle méthode qui repose sur un modèle bayésien génératif probabiliste pour résoudre le problème de l'identification des communautés stables et cohérentes qui se chevauchent dans les réseaux dynamiques. Réseaux synthétiques et réelles sont utilisées pour évaluer la performance par rapport à différents paramètres, la sélection pour modèle, et le moment de l'exécution de l'algorithme proposé. Analyse de la performance indique quel'algorithme proposé dans cette thèse surpasse plusieurs autres algorithmes et révèle l'aperçu inestimable d'un réseau e-mail réelle.
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Livres sur le sujet "Networks dynamic"

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Romano, Aldo, et Giustina Secundo, dir. Dynamic Learning Networks. Boston, MA : Springer US, 2009. http://dx.doi.org/10.1007/978-1-4419-0251-1.

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Klein, Stefan, et Angeliki Poulymenakou, dir. Managing Dynamic Networks. Berlin/Heidelberg : Springer-Verlag, 2006. http://dx.doi.org/10.1007/3-540-32884-x.

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Ran, Bin, et David Boyce. Modeling Dynamic Transportation Networks. Berlin, Heidelberg : Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-642-80230-0.

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Elhoseny, Mohamed, et Aboul Ella Hassanien. Dynamic Wireless Sensor Networks. Cham : Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-92807-4.

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Oteafy, Sharief M. A., et Hossam S. Hassanein. Dynamic Wireless Sensor Networks. Hoboken, NJ, USA : John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118761977.

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Dynamic ad hoc networks. Stevenage : The Institution of Engineering and Technology, 2013.

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Conte, Marco. Dynamic Routing in Broadband Networks. Boston, MA : Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0251-7.

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Gupta, Madan M., Liang Jin et Noriyasu Homma. Static and Dynamic Neural Networks. Hoboken, NJ, USA : John Wiley & Sons, Inc., 2003. http://dx.doi.org/10.1002/0471427950.

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Ash, Gerald R. Dynamic routing in telecommunications networks. New York : McGraw Hill, 1998.

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Conte, Marco. Dynamic Routing in Broadband Networks. Boston, MA : Springer US, 2003.

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Chapitres de livres sur le sujet "Networks dynamic"

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Kolaczyk, Eric D., et Gábor Csárdi. « Dynamic Networks ». Dans Use R !, 207–23. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44129-6_11.

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Kolaczyk, Eric D., et Gábor Csárdi. « Dynamic Networks ». Dans Use R !, 179–95. New York, NY : Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0983-4_10.

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Simmons, Jane M. « Dynamic Optical Networking ». Dans Optical Networks, 349–99. Cham : Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05227-4_8.

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Malkhi, Dahlia. « Dynamic Lookup Networks ». Dans Future Directions in Distributed Computing, 93–96. Berlin, Heidelberg : Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-37795-6_17.

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Meng, Xiaofeng, et Jidong Chen. « Dynamic Transportation Networks ». Dans Moving Objects Management, 137–50. Berlin, Heidelberg : Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13199-8_10.

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Muthuswamy, Bharathwaj, et Santo Banerjee. « Dynamic Nonlinear Networks ». Dans Introduction to Nonlinear Circuits and Networks, 199–314. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-67325-7_4.

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Webb, Geoffrey I., Johannes Fürnkranz, Johannes Fürnkranz, Johannes Fürnkranz, Geoffrey Hinton, Claude Sammut, Joerg Sander et al. « Dynamic Decision Networks ». Dans Encyclopedia of Machine Learning, 298. Boston, MA : Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_235.

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Garces, Freddy, Victor M. Becerra, Chandrasekhar Kambhampati et Kevin Warwick. « Dynamic Neural Networks ». Dans Advances in Industrial Control, 61–99. London : Springer London, 2003. http://dx.doi.org/10.1007/978-1-4471-0065-2_4.

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Wang, Lin. « Dynamic Bayesian Networks ». Dans Encyclopedia of Systems Biology, 619–20. New York, NY : Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_428.

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Davey, Adam, Maximiliane E. Szinovacz et Katherine W. Bauer. « Dynamic care networks ». Dans Diverse Perspectives on Aging in a Changing World, 86–103. London : Routledge, 2017. | Series : Routledge studies in health : Routledge, 2016. http://dx.doi.org/10.4324/9781315638386-7.

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Actes de conférences sur le sujet "Networks dynamic"

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Jha, Devesh K., Thomas A. Wettergren et Asok Ray. « Adaptive Optimal Power Trade-Off in Underwater Sensor Networks ». Dans ASME 2013 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/dscc2013-3717.

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In general, sensor networks have two competing objectives: (i) maximization of network performance with respect to the probability of successful search with a specified false alarm rate for a given coverage area, and (ii) maximization of the network’s operational life. In this context, battery-powered sensing systems are operable as long as they can communicate sensed data to the processing nodes. Since both operations of sensing and communication consume energy, judicious use of these operations could effectively improve the sensor network’s lifetime. From these perspectives, the paper presents an adaptive energy management policy that will optimally allocate the available energy between sensing and communication operations at each node to maximize the network performance under specified constraints. With the assumption of fixed total energy for a sensor network operating over a time period, the problem is reduced to identification of a network topology that maximizes the probability of successful detection of targets over a surveillance region. In a two-stage optimization, a genetic algorithm-based meta-heuristic search is first used to efficiently explore the global design space, and then a local pattern search algorithm is used for convergence to an optimal solution. The results of performance evaluation are presented to validate the proposed concept.
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Wang, Bo, Sergey Nersesov et Hashem Ashrafiuon. « Formation Control for Underactuated Surface Vessel Networks ». Dans ASME 2020 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/dscc2020-3178.

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Abstract Developing distributed control algorithms for multi-agent systems is difficult when each agent is modeled as a nonlinear dynamical system. Moreover, the problem becomes far more complex if the agents do not have sufficient number of actuators to track any arbitrary trajectory. In this paper, we present the first fully decentralized approach to formation control for networks of underactuated surface vessels. The vessels are modeled as three degree of freedom planar rigid bodies with two actuators. Algebraic graph theory is used to model the network as a directed graph and employing a leader-follower model. We take advantage of the cascade structure of the combined nonlinear kinematic and dynamic model of surface vessels and develop a reduced-order error dynamic model using a state transformation definition. The error dynamics and consequently all system states are then stabilized using sliding mode control approach. It is shown that the stabilization of the reduced-order error dynamics guarantees uniform global asymptotic stability of the closed-loop system subject to bounded uncertainties. The proposed control method can be implemented in directed time-invariant communication networks without the availability of global position measurements for any of the vehicles participating in the network. An example of a a network of five surface vessels is simulated to verify the effective performance of the proposed control approach.
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Mohammadi, Rasul, Esmaeil Naderi, Khashayar Khorasani et Shahin Hashtrudi-Zad. « Fault Diagnosis of Gas Turbine Engines by Using Dynamic Neural Networks ». Dans ASME Turbo Expo 2010 : Power for Land, Sea, and Air. ASMEDC, 2010. http://dx.doi.org/10.1115/gt2010-23586.

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This paper presents a novel methodology for fault detection in gas turbine engines based on the concept of dynamic neural networks. The neural network structure belongs to the class of locally recurrent globally feed-forward networks. The architecture of the network is similar to the feed-forward multi-layer perceptron with the difference that the processing units include dynamic characteristics. The dynamics present in these networks make them a powerful tool useful for identification of nonlinear systems. The dynamic neural network architecture that is described in this paper is used for fault detection in a dual-spool turbo fan engine. A number of simulation studies are conducted to demonstrate and verify the advantages of our proposed neural network diagnosis methodology.
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Nadini, Matthieu, Alessandro Rizzo et Maurizio Porfiri. « Contagion Processes Over Temporal Networks With Time-Varying Backbones ». Dans ASME 2019 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/dscc2019-9054.

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Abstract Predicting the diffusion of real-world contagion processes requires a simplified description of human-to-human interactions. Temporal networks offer a powerful means to develop such a mathematically-transparent description. Through temporal networks, one may analytically study the co-evolution of the contagion process and the network topology, as well as incorporate realistic feedback-loop mechanisms related to individual behavioral changes to the contagion. Despite considerable progress, the state-of-the-art does not allow for studying general time-varying networks, where links between individuals dynamically switch to reflect the complexity of social behavior. Here, we tackle this problem by considering a temporal network, in which reducible, associated with node-specific properties, and irreducible links, describing dyadic social ties, simultaneously vary over time. We develop a general mean field theory for the Susceptible-Infected-Susceptible model and conduct an extensive numerical campaign to elucidate the role of network parameters on the average degree of the temporal network and the epidemic threshold. Specifically, we describe how the interplay between reducible and irreducible links influences the disease dynamics, offering insights towards the analysis of complex dynamical networks across science and engineering.
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Yu, Wenchao, Wei Cheng, Charu C. Aggarwal, Haifeng Chen et Wei Wang. « Link Prediction with Spatial and Temporal Consistency in Dynamic Networks ». Dans Twenty-Sixth International Joint Conference on Artificial Intelligence. California : International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/467.

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Dynamic networks are ubiquitous. Link prediction in dynamic networks has attracted tremendous research interests. Many models have been developed to predict links that may emerge in the immediate future from the past evolution of the networks. There are two key factors: 1) a node is more likely to form a link in the near future with another node within its close proximity, rather than with a random node; 2) a dynamic network usually evolves smoothly. Existing approaches seldom unify these two factors to strive for the spatial and temporal consistency in a dynamic network. To address this limitation, in this paper, we propose a link prediction model with spatial and temporal consistency (LIST), to predict links in a sequence of networks over time. LIST characterizes the network dynamics as a function of time, which integrates the spatial topology of network at each timestamp and the temporal network evolution. Comparing to existing approaches, LIST has two advantages: 1) LIST uses a generic model to express the network structure as a function of time, which makes it also suitable for a wide variety of temporal network analysis problems beyond the focus of this paper; 2) by retaining the spatial and temporal consistency, LIST yields better prediction performance. Extensive experiments on four real datasets demonstrate the effectiveness of the LIST model.
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Darabi, Atefe, et Milad Siami. « Dynamic Centrality in Metapopulation Networks : Incorporating Dynamics and Network Structure ». Dans 2023 31st Mediterranean Conference on Control and Automation (MED). IEEE, 2023. http://dx.doi.org/10.1109/med59994.2023.10185681.

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Shi, Min, Yu Huang, Xingquan Zhu, Yufei Tang, Yuan Zhuang et Jianxun Liu. « GAEN : Graph Attention Evolving Networks ». Dans Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California : International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/213.

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Real-world networked systems often show dynamic properties with continuously evolving network nodes and topology over time. When learning from dynamic networks, it is beneficial to correlate all temporal networks to fully capture the similarity/relevance between nodes. Recent work for dynamic network representation learning typically trains each single network independently and imposes relevance regularization on the network learning at different time steps. Such a snapshot scheme fails to leverage topology similarity between temporal networks for progressive training. In addition to the static node relationships within each network, nodes could show similar variation patterns (e.g., change of local structures) within the temporal network sequence. Both static node structures and temporal variation patterns can be combined to better characterize node affinities for unified embedding learning. In this paper, we propose Graph Attention Evolving Networks (GAEN) for dynamic network embedding with preserved similarities between nodes derived from their temporal variation patterns. Instead of training graph attention weights for each network independently, we allow model weights to share and evolve across all temporal networks based on their respective topology discrepancies. Experiments and validations, on four real-world dynamic graphs, demonstrate that GAEN outperforms the state-of-the-art in both link prediction and node classification tasks.
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Motato, Eliot, et Clark Radcliffe. « Recursive Assembly of Multi-Layer Perceptron Neural Networks ». Dans ASME 2014 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/dscc2014-5997.

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The objective of this paper is to present a methodology to modularly connect Multi-Layer Perceptron (MLP) neural network models describing static port-based physical behavior. The MLP considered in this work are characterized for an standard format with a single hidden layer with sigmoidal activation functions. Since every port is defined by an input-output pair, the number of outputs of the proposed neural network format is equal to the number of its inputs. This work extends the Model Assembly Method (MAM) used to connect transfer function models and Volterra models to multi-layer perceptron neural networks.
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Benzaoui, N., M. Szczerban Gonzalez, J. M. Estarán, H. Mardoyan, W. Lautenschlaeger, U. Gebhard, L. Dembeck, S. Bigo et Y. Pointurier. « Latency control in Deterministic and Dynamic Networks ». Dans Photonic Networks and Devices. Washington, D.C. : OSA, 2019. http://dx.doi.org/10.1364/networks.2019.net3d.4.

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Cai, Shaofeng, Yao Shu et Wei Wang. « Dynamic Routing Networks ». Dans 2021 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2021. http://dx.doi.org/10.1109/wacv48630.2021.00363.

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Rapports d'organisations sur le sujet "Networks dynamic"

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Pearl, Judea. Dynamic Constraint Networks. Fort Belvoir, VA : Defense Technical Information Center, février 1994. http://dx.doi.org/10.21236/ada278396.

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Pearl, Judea. Dynamic Constraints Networks. Fort Belvoir, VA : Defense Technical Information Center, octobre 1989. http://dx.doi.org/10.21236/ada219778.

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Weischedel, Ralph. Extracting Dynamic Evidence Networks. Fort Belvoir, VA : Defense Technical Information Center, décembre 2004. http://dx.doi.org/10.21236/ada429898.

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Polydoros, Andreas, Gaylord K. Huth et Unjeng Cheng. Dynamic Jamming of Networks. Fort Belvoir, VA : Defense Technical Information Center, mars 1990. http://dx.doi.org/10.21236/ada223044.

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Turcotte, Melissa. Anomaly Detection in Dynamic Networks. Office of Scientific and Technical Information (OSTI), octobre 2014. http://dx.doi.org/10.2172/1160097.

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Field, Richard V.,, Hamilton E. Link, Jacek Skryzalin et Jeremy D. Wendt. A dynamic model for social networks. Office of Scientific and Technical Information (OSTI), septembre 2018. http://dx.doi.org/10.2172/1472229.

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Cheng, Unjeng. Static and Dynamic Jamming of Networks. Fort Belvoir, VA : Defense Technical Information Center, décembre 1987. http://dx.doi.org/10.21236/ada188921.

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Moore, Allison. Centrality Measures of Dynamic Social Networks. Fort Belvoir, VA : Defense Technical Information Center, novembre 2012. http://dx.doi.org/10.21236/ada571973.

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Polydoros, Andreas. Packet Radio Networks under Dynamic Jamming. Fort Belvoir, VA : Defense Technical Information Center, décembre 1989. http://dx.doi.org/10.21236/ada217094.

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Groves, Taylor, et Ryan Grant. Power Aware Dynamic Provisioning of HPC Networks. Office of Scientific and Technical Information (OSTI), octobre 2015. http://dx.doi.org/10.2172/1331496.

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