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Journal articles on the topic 'Networks dynamic'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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1

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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2

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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3

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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4

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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9

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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10

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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11

Sun, Zejun, Jinfang Sheng, Bin Wang, Aman Ullah, and FaizaRiaz Khawaja. "Identifying Communities in Dynamic Networks Using Information Dynamics." Entropy 22, no. 4 (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

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12

Zhang, Guoyin, Xu Fan, and Yanxia Wu. "Minimal Increase Network Coding for Dynamic Networks." PLOS ONE 11, no. 2 (2016): e0148725. http://dx.doi.org/10.1371/journal.pone.0148725.

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13

Dawaliby, Samir, Abbas Bradai, and Yannis Pousset. "Adaptive dynamic network slicing in LoRa networks." Future Generation Computer Systems 98 (September 2019): 697–707. http://dx.doi.org/10.1016/j.future.2019.01.042.

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14

Melamed, David, Ashley Harrell, and Brent Simpson. "Cooperation, clustering, and assortative mixing in dynamic networks." Proceedings of the National Academy of Sciences 115, no. 5 (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 reputation

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15

Torgashev, Valery Antony. "Dynamic Automata Networks." SPIIRAS Proceedings 4, no. 27 (2014): 23. http://dx.doi.org/10.15622/sp.27.2.

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16

Donner, Amy. "GRBing dynamic networks." Nature Chemical Biology 7, no. 9 (2011): 576. http://dx.doi.org/10.1038/nchembio.650.

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17

Kuperman, M. N., M. Ballard, and F. Laguna. "Dynamic domain networks." European Physical Journal B 50, no. 3 (2006): 513–20. http://dx.doi.org/10.1140/epjb/e2006-00148-3.

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18

van Assen, M. A. L. M., and Arnout van de Rijt. "Dynamic exchange networks." Social Networks 29, no. 2 (2007): 266–78. http://dx.doi.org/10.1016/j.socnet.2006.12.003.

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19

Khalili, A. M. "Dynamic Switching Networks." Complex Systems 28, no. 1 (2019): 77–96. http://dx.doi.org/10.25088/complexsystems.28.1.77.

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20

Harrell, Ashley, David Melamed, and Brent Simpson. "The strength of dynamic ties: The ability to alter some ties promotes cooperation in those that cannot be altered." Science Advances 4, no. 12 (2018): eaau9109. http://dx.doi.org/10.1126/sciadv.aau9109.

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Dynamic networks, where ties can be shed and new ties can be formed, promote the evolution of cooperation. Yet, past research has only compared networks where all ties can be severed to those where none can, confounding the benefits of fully dynamic networks with the presence of some dynamic ties within the network. Further, humans do not live in fully dynamic networks. Instead, in real-world networks, some ties are subject to change, while others are difficult to sever. Here, we consider whether and how cooperation evolves in networks containing both static and dynamic ties. We argue and find

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21

Zhu, Zhiqiang. "Control Analysis of Propagation Dynamics on Networks." Journal of Physics: Conference Series 2224, no. 1 (2022): 012092. http://dx.doi.org/10.1088/1742-6596/2224/1/012092.

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Abstract It is generally the dynamic behavior of multiple information in the network. Based on the principle of propagation dynamics and mathematical model, this paper simulates the dynamic process of information in the network, and analyzes the influence of network structure and propagation dynamics on the dynamic behavior of information in the network through the simulation results. By simulating the dynamic process of communication, we find that the location and release time of intervention information in the network will have an impact, and we can control the dynamic behavior of informatio

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22

Britton, Tom, and Mathias Lindholm. "Dynamic Random Networks in Dynamic Populations." Journal of Statistical Physics 139, no. 3 (2010): 518–35. http://dx.doi.org/10.1007/s10955-010-9952-5.

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23

Nie, Chun-Xiao. "Hurst analysis of dynamic networks." Chaos: An Interdisciplinary Journal of Nonlinear Science 32, no. 2 (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

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24

SMITH, DAVID M. D., JUKKA-PEKKA ONNELA, CHIU FAN LEE, MARK D. FRICKER, and NEIL F. JOHNSON. "NETWORK AUTOMATA: COUPLING STRUCTURE AND FUNCTION IN DYNAMIC NETWORKS." Advances in Complex Systems 14, no. 03 (2011): 317–39. http://dx.doi.org/10.1142/s0219525911003050.

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We introduce Network Automata, a framework which couples the topological evolution of a network to its structure. To demonstrate its implementation we describe a simple model which exhibits behavior similar to the "Game of Life" before recasting some simple, well-known network models as Network Automata. We then introduce Functional Network Automata which are useful for dealing with networks in which the topology evolves according to some specified microscopic rules and, simultaneously, there is a dynamic process taking place on the network that both depends on its structure but is also capabl

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25

Flossdorf, Jonathan, and Carsten Jentsch. "Change Detection in Dynamic Networks Using Network Characteristics." IEEE Transactions on Signal and Information Processing over Networks 7 (2021): 451–64. http://dx.doi.org/10.1109/tsipn.2021.3094900.

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26

Zhang, Jiawei, Yuefeng Ji, Mei Song, et al. "Dynamic Virtual Network Embedding Over Multilayer Optical Networks." Journal of Optical Communications and Networking 7, no. 9 (2015): 918. http://dx.doi.org/10.1364/jocn.7.000918.

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27

Lang, Gai Ping, Yu Bin Xu, and Lin Ma. "Dynamic Network Selection in Vehicular Heterogeneous Wireless Networks." Advanced Engineering Forum 5 (July 2012): 128–32. http://dx.doi.org/10.4028/www.scientific.net/aef.5.128.

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Three general types of applications, safety applications, traffic applications and non-safety applications, are developed over vehicular networks. Different usage frequency may occur for different applications in vehicular networks. Most of current researches only focus on the safety applications, traffic applications or non-safety applications. Actually, these applications are used by one vehicular according to his dynamic requirements. So, this paper takes these applications together into account, and corresponding access policy is proposed.

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28

Wang, Changda, and Elisa Bertino. "Sensor Network Provenance Compression Using Dynamic Bayesian Networks." ACM Transactions on Sensor Networks 13, no. 1 (2017): 1–32. http://dx.doi.org/10.1145/2997653.

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29

Yelithoti, Sravana Kumar, Tapaswini Samant, and Swati Swayamsiddha. "Advancing communication networks: integrating ‎enhanced knowledge mapping with hybrid deep ‎recurrent neural networks for dynamic spectrum ‎access." International Journal of Basic and Applied Sciences 14, no. 1 (2025): 291–303. https://doi.org/10.14419/5y394g91.

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By merging knowledge mapping using Hybrid Deep Recurrent Neural Networks (RNNs), our suggested method maximizes dynamic ‎spectrum access in diverse networks. Optimizing the assignment of spectrum resources while enabling different device characteristics ‎and network circumstances is our approach to addressing the issues of spectrum allocation in different network situations. Improved ‎processing capabilities at the network's edge enable real time monitoring of patterns in spectrum consumption. In order to dynamically ‎allocate spectrum according to user demands and network dynamics, our dynami

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30

Mouhamadou Wade, Ahmed. "EXPLORATION WITH RETURN OF HIGHLY DYNAMIC NETWORKS." International Journal of Advanced Research 9, no. 10 (2021): 315–19. http://dx.doi.org/10.21474/ijar01/13550.

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In this paper, we study the necessary and sufficient time to explore with return constantly connected dynamic networks modelled by a dynamic graphs. Exploration with return consists, for an agent operating in a dynamic graph, of visiting all the vertices of the graph and returning to the starting vertex. We show that for constantly connected dynamic graphs based on a ring of sizen,3n-4 time units are necessary and sufficient to explore it. Assuming that the agent knows the dynamics of the graph.

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31

Chen, Kevin S. "Optimal Population Coding for Dynamic Input by Nonequilibrium Networks." Entropy 24, no. 5 (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 dyn

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Chen, Kevin S. "Optimal Population Coding for Dynamic Input by Nonequilibrium Networks." Entropy 24, no. 5 (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 dyn

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33

Chen, Kevin S. "Optimal Population Coding for Dynamic Input by Nonequilibrium Networks." Entropy 24, no. 5 (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 dyn

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34

Ortiz, Santiago. "Dynamic Visualization of Networks." Leonardo 47, no. 3 (2014): 274. http://dx.doi.org/10.1162/leon_a_00777.

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Networks found in nature, culture and technology are dynamic in multiple ways. This poses several challenges to the field of networks visualization that, in general, has been representing networks with fixed layouts. By depicting networks statically, not only are the dynamic properties lost, it is also difficult to read basic properties such as the number of connections between nodes. The author proposes a series of interactive techniques to visualize networks, aimed to reveal their dynamic and organic nature.

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35

Perrin, B. E., L. Ralaivola, A. Mazurie, S. Bottani, J. Mallet, and F. d'Alche-Buc. "Gene networks inference using dynamic Bayesian networks." Bioinformatics 19, Suppl 2 (2003): ii138—ii148. http://dx.doi.org/10.1093/bioinformatics/btg1071.

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36

KATADA, NAOFUMI, and HARUHIKO NISHIMURA. "DYNAMIC MEMORIZATION CHARACTERISTICS IN NEURAL NETWORKS WITH DIFFERENT NEURONAL DYNAMICS." International Journal of Neural Systems 17, no. 03 (2007): 161–70. http://dx.doi.org/10.1142/s0129065707001032.

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We introduce a stimulus-response scheme that supports plastic variation of synapse weights in neural networks, and analyze how memory formation evolves under external stimulation. In so doing, chaotic networks and stochastic networks that have very different dynamics are compared. Experimental results suggest that chaotic activity remarkably outperforms stochastic activity in stimulus-response memorization. This seems to be indicative of effectiveness of the chaos in dynamic learning by stimulus-response scheme oriented to natural learning.

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37

Tchangani, Ayeley P., and Daniel Noyes. "Modeling dynamic reliability using dynamic Bayesian networks." Journal Européen des Systèmes Automatisés 40, no. 8 (2006): 915–35. http://dx.doi.org/10.3166/jesa.40.915-935.

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38

Tsodyks, Misha, Klaus Pawelzik, and Henry Markram. "Neural Networks with Dynamic Synapses." Neural Computation 10, no. 4 (1998): 821–35. http://dx.doi.org/10.1162/089976698300017502.

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Transmission across neocortical synapses depends on the frequency of presynaptic activity (Thomson & Deuchars, 1994). Interpyramidal synapses in layer V exhibit fast depression of synaptic transmission, while other types of synapses exhibit facilitation of transmission. To study the role of dynamic synapses in network computation, we propose a unified phenomenological model that allows computation of the postsynaptic current generated by both types of synapses when driven by an arbitrary pattern of action potential (AP) activity in a presynaptic population. Using this formalism, we analyze

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39

Foley, Michael, Patrick Forber, Rory Smead, and Christoph Riedl. "Conflict and convention in dynamic networks." Journal of The Royal Society Interface 15, no. 140 (2018): 20170835. http://dx.doi.org/10.1098/rsif.2017.0835.

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An important way to resolve games of conflict (snowdrift, hawk–dove, chicken) involves adopting a convention: a correlated equilibrium that avoids any conflict between aggressive strategies. Dynamic networks allow individuals to resolve conflict via their network connections rather than changing their strategy. Exploring how behavioural strategies coevolve with social networks reveals new dynamics that can help explain the origins and robustness of conventions. Here, we model the emergence of conventions as correlated equilibria in dynamic networks. Our results show that networks have the tend

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40

Han, Yueying, Yi Cao, and Hai Lei. "Dynamic Covalent Hydrogels: Strong yet Dynamic." Gels 8, no. 9 (2022): 577. http://dx.doi.org/10.3390/gels8090577.

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Hydrogels are crosslinked polymer networks with time-dependent mechanical response. The overall mechanical properties are correlated with the dynamics of the crosslinks. Generally, hydrogels crosslinked by permanent chemical crosslinks are strong but static, while hydrogels crosslinked by physical interactions are weak but dynamic. It is highly desirable to create synthetic hydrogels that possess strong mechanical stability yet remain dynamic for various applications, such as drug delivery cargos, tissue engineering scaffolds, and shape-memory materials. Recently, with the introduction of dyna

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41

Miao, Jingcheng, Na Lv, Kefan Chen, Qi Gao, and Xiang Wang. "Dynamic Reliability-Aware Virtual Network Embedding for Airborne Tactical Networks." Wireless Communications and Mobile Computing 2022 (September 16, 2022): 1–19. http://dx.doi.org/10.1155/2022/7164854.

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Airborne tactical networks (ATNs) built on network virtualization (NV) can enable efficient information sharing for network-centric warfare by breaking the tight coupling between applications and network infrastructure and thus solving the network ossification problem. With dynamic changes during military missions, the application of virtualization is challenged by the changing demands on network resources when instantiating multiple virtual networks (VNs) on a shared substrate network (SN), known as virtual network embedding (VNE). However, existing dynamic VNE algorithms, mostly designed for

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42

Perova, Juю P., V. P. Grigoriev, and D. O. Zhukov. "Models and methods for analyzing complex networks and social network structures." Russian Technological Journal 11, no. 2 (2023): 33–49. http://dx.doi.org/10.32362/2500-316x-2023-11-2-33-49.

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Objectives. The study aimed to investigate contemporary models, methods, and tools used for analyzing complex social network structures, both on the basis of ready-made solutions in the form of services and software, as well as proprietary applications developed using the Python programming language. Such studies make it possible not only to predict the dynamics of social processes (changes in social attitudes), but also to identify trends in socioeconomic development by monitoring users’ opinions on important economic and social issues, both at the level of individual territorial entities (fo

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Ma, Qianli, Wanqing Zhuang, Sen Li, Desen Huang, and Garrison Cottrell. "Adversarial Dynamic Shapelet Networks." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 5069–76. http://dx.doi.org/10.1609/aaai.v34i04.5948.

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Shapelets are discriminative subsequences for time series classification. Recently, learning time-series shapelets (LTS) was proposed to learn shapelets by gradient descent directly. Although learning-based shapelet methods achieve better results than previous methods, they still have two shortcomings. First, the learned shapelets are fixed after training and cannot adapt to time series with deformations at the testing phase. Second, the shapelets learned by back-propagation may not be similar to any real subsequences, which is contrary to the original intention of shapelets and reduces model

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Sanghai, S., P. Domingos, and D. Weld. "Relational Dynamic Bayesian Networks." Journal of Artificial Intelligence Research 24 (December 2, 2005): 759–97. http://dx.doi.org/10.1613/jair.1625.

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Stochastic processes that involve the creation of objects and relations over time are widespread, but relatively poorly studied. For example, accurate fault diagnosis in factory assembly processes requires inferring the probabilities of erroneous assembly operations, but doing this efficiently and accurately is difficult. Modeled as dynamic Bayesian networks, these processes have discrete variables with very large domains and extremely high dimensionality. In this paper, we introduce relational dynamic Bayesian networks (RDBNs), which are an extension of dynamic Bayesian networks (DBNs) to fir

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45

Benzaoui, Nihel, Mijail Szczerban Gonzalez, Jose Manuel Estaran, et al. "Deterministic Dynamic Networks (DDN)." Journal of Lightwave Technology 37, no. 14 (2019): 3465–74. http://dx.doi.org/10.1109/jlt.2019.2917280.

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46

Hofte, G. Henri ter, and Ingrid Mulder. "Dynamic personal social networks." ACM SIGGROUP Bulletin 24, no. 3 (2003): 139–42. http://dx.doi.org/10.1145/1052829.1052830.

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47

Manessi, Franco, Alessandro Rozza, and Mario Manzo. "Dynamic graph convolutional networks." Pattern Recognition 97 (January 2020): 107000. http://dx.doi.org/10.1016/j.patcog.2019.107000.

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48

Kevin Cullinane. "Modeling dynamic transportation networks." Journal of Transport Geography 6, no. 1 (1998): 76–78. http://dx.doi.org/10.1016/s0966-6923(98)90041-2.

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Si, J., S. Lin, and M. A. Vuong. "Dynamic topology representing networks." Neural Networks 13, no. 6 (2000): 617–27. http://dx.doi.org/10.1016/s0893-6080(00)00039-3.

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Durante, Daniele, and David B. Dunson. "Locally adaptive dynamic networks." Annals of Applied Statistics 10, no. 4 (2016): 2203–32. http://dx.doi.org/10.1214/16-aoas971.

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