Relevant bibliographies by topics / Networks dynamic

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Networks dynamic'

Author: Grafiati
Published: 25 May 2024
Last updated: 31 July 2025

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Journal articles on the topic "Networks dynamic"

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Iedema, Rick, Raj Verma, Sonia Wutzke, Nigel Lyons, and Brian McCaughan. "A network of networks." Journal of Health Organization and Management 31, no. 2 (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 achievemen
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CHIU, CHINCHUAN, and MICHAEL A. SHANBLATT. "HUMAN-LIKE DYNAMIC PROGRAMMING NEURAL NETWORKS FOR DYNAMIC TIME WARPING SPEECH RECOGNITION." International Journal of Neural Systems 06, no. 01 (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 presente
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Kuhn, Fabian, and Rotem Oshman. "Dynamic networks." ACM SIGACT News 42, no. 1 (2011): 82–96. http://dx.doi.org/10.1145/1959045.1959064.

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Yan, Xian. "Key Factors Influencing Network Resilience in Dynamical Networks." Frontiers in Computing and Intelligent Systems 3, no. 3 (2023): 99–101. http://dx.doi.org/10.54097/fcis.v3i3.8577.

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There has been much recent research focusing on the resilience of networks, providing theoretical insights into the effective response of real-world systems systems to disasters. However, few studies have analyzed the factors that affect the resilience of networks. And the network operation process varies greatly so that the dynamic behavior of the network is a factor that has to be considered. To bridge these gaps, we analyze the factors affecting dynamic network resilience in terms of network dynamics. There are two main influencing factors: differentiation of failure probability, differenti
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Zhong, Rui, Lin Yi, Xiarui Wang, Weijun Shu, and Liang Yue. "Bioinspired nucleic acid-based dynamic networks for signal dynamics." Chemical Synthesis 3, no. 3 (2023): 27. http://dx.doi.org/10.20517/cs.2023.15.

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Signaling dynamic networks in living systems determine the conversion of environmental information into biological activities. Systems chemistry, focusing on studying complex chemical systems, promotes the connections between chemistry and biology and provides a new way to mimic these signaling dynamic processes by designing artificial networks and understanding their emerging properties and functions that are absent in isolated molecules. Nucleic acids, while relatively simple in their design and synthesis, encode rich structural and functional information in their base sequence, which makes
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Levin, Ilya, Mark Korenblit, and Vadim Talis. "STUDY OF SOCIAL NETWORKS’ DYNAMICS BY SIMULATION WITHIN THE NODEXL-EXCEL ENVIRONMENT." Problems of Education in the 21st Century 54, no. 1 (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 Exc
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Ponraj, Ranjana, and George Amalanathan. "Dynamic Capacity Routing in Networks with MTSP." International Journal of Computer and Communication Engineering 5, no. 6 (2016): 465–72. http://dx.doi.org/10.17706/ijcce.2016.5.6.465-472.

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Elamurugu, V., and D. J. Evanjaline. "DynAuthRoute: Dynamic Security for Wireless Sensor Networks." Indian Journal Of Science And Technology 17, no. 13 (2024): 1323–30. http://dx.doi.org/10.17485/ijst/v17i13.49.

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Objectives: The research aims to design an architecture for secure transmission of data in wireless sensor networks. Methods: The method involves three main pillars: authentication, data encryption, and dynamic routing. Extensive simulations have been conducted to evaluate the suggested method in terms of energy consumption, memory footprint, packet delivery ratio, end-to-end latency, execution time, encryption time, and decryption time. Findings: For authentication, a dynamic key is used to power an improved salt password hashing method. Data encryption is performed using format-preserving en
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Galizia, Roberto, and Petri T. Piiroinen. "Regions of Reduced Dynamics in Dynamic Networks." International Journal of Bifurcation and Chaos 31, no. 06 (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 dyna
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Gupta, Pramod, and Naresh K. Sinha. "Modeling Robot Dynamics Using Dynamic Neural Networks." IFAC Proceedings Volumes 30, no. 11 (1997): 755–59. http://dx.doi.org/10.1016/s1474-6670(17)42936-3.

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Dissertations / Theses on the topic "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
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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.<br>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 ap
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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.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 139-145).<br>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 ove
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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
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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 shou
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Books on the topic "Networks dynamic"

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

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

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

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

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

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

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

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

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

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Ash, Gerald R. Dynamic routing in telecommunications networks. McGraw-Hill, 1997.

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Book chapters on the topic "Networks dynamic"

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Kolaczyk, Eric D., and Gábor Csárdi. "Dynamic Networks." In Use R! Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44129-6_11.

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Kolaczyk, Eric D., and Gábor Csárdi. "Dynamic Networks." In Use R! 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." In Optical Networks. 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." In Future Directions in Distributed Computing. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-37795-6_17.

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Meng, Xiaofeng, and Jidong Chen. "Dynamic Transportation Networks." In Moving Objects Management. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13199-8_10.

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Muthuswamy, Bharathwaj, and Santo Banerjee. "Dynamic Nonlinear Networks." In Introduction to Nonlinear Circuits and Networks. 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, et al. "Dynamic Decision Networks." In Encyclopedia of Machine Learning. 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, and Kevin Warwick. "Dynamic Neural Networks." In Advances in Industrial Control. Springer London, 2003. http://dx.doi.org/10.1007/978-1-4471-0065-2_4.

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Wang, Lin. "Dynamic Bayesian Networks." In Encyclopedia of Systems Biology. 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, and Katherine W. Bauer. "Dynamic care networks." In Diverse Perspectives on Aging in a Changing World. Routledge, 2016. http://dx.doi.org/10.4324/9781315638386-7.

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Conference papers on the topic "Networks dynamic"

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Jha, Devesh K., Thomas A. Wettergren, and Asok Ray. "Adaptive Optimal Power Trade-Off in Underwater Sensor Networks." In 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 presen
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Wang, Bo, Sergey Nersesov, and Hashem Ashrafiuon. "Formation Control for Underactuated Surface Vessel Networks." In 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 l
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Mohammadi, Rasul, Esmaeil Naderi, Khashayar Khorasani, and Shahin Hashtrudi-Zad. "Fault Diagnosis of Gas Turbine Engines by Using Dynamic Neural Networks." In 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 fau
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Yu, Wenchao, Wei Cheng, Charu C. Aggarwal, Haifeng Chen, and Wei Wang. "Link Prediction with Spatial and Temporal Consistency in Dynamic Networks." In Twenty-Sixth International Joint Conference on Artificial Intelligence. 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 limit
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Nadini, Matthieu, Alessandro Rizzo, and Maurizio Porfiri. "Contagion Processes Over Temporal Networks With Time-Varying Backbones." In 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 betwe
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Penaloza, Emiliano, and Nathaniel Stevens. "Changepoint Detection in Highly-Atributed Dynamic Graphs." In LatinX in AI at International Conference on Machine Learning 2024. Journal of LatinX in AI Research, 2024. http://dx.doi.org/10.52591/lxai202407278.

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Detecting anomalous behavior in dynamic networks remains a constant challenge. This problem is further exacerbated when the underlying topology of these networks is affected by individual highly-dimensional node attributes. We address this issue by tracking a network’s modularity as a proxy of its community structure. We leverage Graph Neural Networks (GNNs) to estimate each snapshot’s modularity. GNNs can account for both network structure and high-dimensional node attributes, providing a comprehensive approach for estimating network statistics. Our method is validated through simulations tha
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Shi, Min, Yu Huang, Xingquan Zhu, Yufei Tang, Yuan Zhuang, and Jianxun Liu. "GAEN: Graph Attention Evolving Networks." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. 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 stati
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Darabi, Atefe, and Milad Siami. "Dynamic Centrality in Metapopulation Networks: Incorporating Dynamics and Network Structure." In 2023 31st Mediterranean Conference on Control and Automation (MED). IEEE, 2023. http://dx.doi.org/10.1109/med59994.2023.10185681.

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Motato, Eliot, and Clark Radcliffe. "Recursive Assembly of Multi-Layer Perceptron Neural Networks." In 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
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Benzaoui, N., M. Szczerban Gonzalez, J. M. Estarán, et al. "Latency control in Deterministic and Dynamic Networks." In Photonic Networks and Devices. OSA, 2019. http://dx.doi.org/10.1364/networks.2019.net3d.4.

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Reports on the topic "Networks dynamic"

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

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

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

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Polydoros, Andreas, Gaylord K. Huth, and Unjeng Cheng. Dynamic Jamming of Networks. Defense Technical Information Center, 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), 2014. http://dx.doi.org/10.2172/1160097.

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

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

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

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

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

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