Статті в журналах: "Time-varying network"

1

Crane, Harry. "Time-varying network models." Bernoulli 21, no. 3 (August 2015): 1670–96. http://dx.doi.org/10.3150/14-bej617.

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2

Yu, Hui, Yi Zhang, and Gao Yang Liu. "Multi-Agent Consensus with a Time-Varying Reference State and Time-Varying Delays." Applied Mechanics and Materials 48-49 (February 2011): 724–29. http://dx.doi.org/10.4028/www.scientific.net/amm.48-49.724.

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This paper is devoted to the study of consensus problem of multi-agent systems with a time-varying reference state in directed networks with both switching topology and time-delay. Stability analysis is performed based on a proposed Lyapunov–Krasovskii function. Sufficient conditions based on linear matrix inequalities (LMIs) are given to guarantee that multi-agent consensus on a time-varying reference state can be achieved under arbitrary switching of the network topology even if the network communication is affected by time-delay. These consensus algorithms are also extended to consensus formation among the agents. Finally, simulation example is given to validate our theoretical results.

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3

Lucas, Maxime, Duccio Fanelli, Timoteo Carletti, and Julien Petit. "Desynchronization induced by time-varying network." EPL (Europhysics Letters) 121, no. 5 (May 10, 2018): 50008. http://dx.doi.org/10.1209/0295-5075/121/50008.

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4

Carchiolo, Vincenza, Christian Cavallo, Marco Grassia, Michele Malgeri, and Giuseppe Mangioni. "Link Prediction in Time Varying Social Networks." Information 13, no. 3 (March 1, 2022): 123. http://dx.doi.org/10.3390/info13030123.

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Predicting new links in complex networks can have a large societal impact. In fact, many complex systems can be modeled through networks, and the meaning of the links depend on the system itself. For instance, in social networks, where the nodes are users, links represent relationships (such as acquaintance, friendship, etc.), whereas in information spreading networks, nodes are users and content and links represent interactions, diffusion, etc. However, while many approaches involve machine learning-based algorithms, just the most recent ones account for the topology of the network, e.g., geometric deep learning techniques to learn on graphs, and most of them do not account for the temporal dynamics in the network but train on snapshots of the system at a given time. In this paper, we aim to explore Temporal Graph Networks (TGN), a Graph Representation Learning-based approach that natively supports dynamic graphs and assigns to each event (link) a timestamp. In particular, we investigate how the TGN behaves when trained under different temporal granularity or with various event aggregation techniques when learning the inductive and transductive link prediction problem on real social networks such as Twitter, Wikipedia, Yelp, and Reddit. We find that initial setup affects the temporal granularity of the data, but the impact depends on the specific social network. For instance, we note that the train batch size has a strong impact on Twitter, Wikipedia, and Yelp, while it does not matter on Reddit.

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5

Wang, Li Fu, Jian Ding, and Zhi Kong. "Local Synchronization for Time Varying Topological Networks." Advanced Materials Research 850-851 (December 2013): 545–48. http://dx.doi.org/10.4028/www.scientific.net/amr.850-851.545.

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In this paper, local synchronization problem of complex networks is theoretically and numerically studied. Base on the Lyapunov stability theory, a sufficient criterion for local synchronization of complex network which have the time-varying connection topologies is derived via designed decentralized linear controllers. And a numerical example of typical the Rössler network system with time-varying linear coupling has been used to demonstrate and verify the proposed. And, the simulation results show the effectiveness of proposed synchronization approaches.

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6

Hammachukiattikul, Porpattama. "Finite-time Stability, Dissipativity and Passivity Analysis of Discrete-time Neural Networks Time-varying Delays." Emerging Science Journal 3, no. 6 (December 1, 2019): 361–68. http://dx.doi.org/10.28991/esj-2019-01198.

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The neural network time-varying delay was described as the dynamic properties of a neural cell, including neural functional and neural delay differential equations. The differential expression explains the derivative term of current and past state. The objective of this paper obtained the neural network time-varying delay. A delay-dependent condition is provided to ensure the considered discrete-time neural networks with time-varying delays to be finite-time stability, dissipativity, and passivity. This paper using a new Lyapunov-Krasovskii functional as well as the free-weighting matrix approach and a linear matrix inequality analysis (LMI) technique constructing to a novel sufficient criterion on finite-time stability, dissipativity, and passivity of the discrete-time neural networks with time-varying delays for improving. We propose sufficient conditions for discrete-time neural networks with time-varying delays. An effective LMI approach derives by base the appropriate type of Lyapunov functional. Finally, we present the effectiveness of novel criteria of finite-time stability, dissipativity, and passivity condition of discrete-time neural networks with time-varying delays in the form of linear matrix inequality (LMI).

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7

Sharma, Vicky, Koushik Kar, Richard La, and Leandros Tassiulas. "Dynamic network provisioning for time-varying traffic." Journal of Communications and Networks 9, no. 4 (December 2007): 408–18. http://dx.doi.org/10.1109/jcn.2007.6182876.

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8

Zhaoyan Wu and Xiaoli Gong. "IMPULSIVE SYNCHRONIZATION OF TIME-VARYING DYNAMICAL NETWORK." Journal of Applied Analysis & Computation 6, no. 1 (2016): 94–102. http://dx.doi.org/10.11948/2016008.

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9

Myung, Hyun, and Jong-Hwan Kim. "Time-Varying Two-Phase Optimization Neural Network." Journal of Intelligent and Fuzzy Systems 5, no. 2 (1997): 85–101. http://dx.doi.org/10.3233/ifs-1997-5201.

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10

Khamfroush, Hana, Daniel E. Lucani, Joao Barros, and Peyman Pahlevani. "Network-Coded Cooperation Over Time-Varying Channels." IEEE Transactions on Communications 62, no. 12 (December 2014): 4413–25. http://dx.doi.org/10.1109/tcomm.2014.2367016.

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11

Nasrabadi, Ebrahim, and S. Mehdi Hashemi. "Minimum cost time-varying network flow problems." Optimization Methods and Software 25, no. 3 (June 2010): 429–47. http://dx.doi.org/10.1080/10556780903239121.

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12

Cao, Jin, Drew Davis, Scott Vander Wiel, and Bin Yu. "Time-Varying Network Tomography: Router Link Data." Journal of the American Statistical Association 95, no. 452 (December 2000): 1063–75. http://dx.doi.org/10.1080/01621459.2000.10474303.

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13

KUMARI, SUCHI, and ANURAG SINGH. "TIME-VARYING NETWORK MODELING AND ITS OPTIMAL ROUTING STRATEGY." Advances in Complex Systems 21, no. 02 (March 2018): 1850006. http://dx.doi.org/10.1142/s0219525918500066.

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Since all the existing real world networks are evolving, the study of traffic dynamics is a challenging task. Avoidance of traffic congestion, system utility maximization and enhancement of network capacity are prominent issues. Network capacity may be improved either by optimizing network topology or enhancing in routing approach. In this context, we propose and design a model of the time-varying data communication networks (TVCN) based on the dynamics of inflowing links. Traffic congestion can be avoided by using a suitable centrality measure, especially betweenness and Eigen vector centralities. If the nodes coming in user’s route are most betweenness central, then that route will be highly congested. Eigen vector centrality is used to find the influence of a node on others. If a node is most influential, then it will be highly congested and considered as least reputed. For that reason, routes are chosen such that the sum of the centralities of the nodes coming in user’s route should be minimum. Furthermore, Kelly’s optimization formulation for a rate allocation problem is used for obtaining optimal rates of distinct users at different time instants and it is found that the user’s path with lowest betweenness centrality and highest reputation will always give maximum rate at the stable point.

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14

Wang, Xue-Zhong, Yimin Wei, and Predrag S. Stanimirović. "Complex Neural Network Models for Time-Varying Drazin Inverse." Neural Computation 28, no. 12 (December 2016): 2790–824. http://dx.doi.org/10.1162/neco_a_00866.

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Two complex Zhang neural network (ZNN) models for computing the Drazin inverse of arbitrary time-varying complex square matrix are presented. The design of these neural networks is based on corresponding matrix-valued error functions arising from the limit representations of the Drazin inverse. Two types of activation functions, appropriate for handling complex matrices, are exploited to develop each of these networks. Theoretical results of convergence analysis are presented to show the desirable properties of the proposed complex-valued ZNN models. Numerical results further demonstrate the effectiveness of the proposed models.

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15

Ríos-Rivera, Daniel, Jorge D. Rios, Oscar D. Sanchez, and Alma Y. Alanis. "Impulsive Pinning Control of Discrete-Time Complex Networks with Time-Varying Connections." Mathematics 10, no. 21 (November 1, 2022): 4051. http://dx.doi.org/10.3390/math10214051.

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Complex dynamical networks with time-varying connections have characteristics that allow a better representation of real-world complex systems, especially interest in their not static behavior and topology. Their applications reach areas such as communication systems, electrical systems, medicine, robotic, and more. Both continuous and discrete-time complex dynamical networks and the pinning control technique have been studied. However, even with interest in the research on complex networks combining characteristics of discrete-time, time-varying connections, pinning control, and impulsive control, there are few studies reported in the literature. There are some previous studies dealing with impulsively pin-controlling a discrete-time complex network. Nevertheless, they neglect to deal with time-varying connections; they deal with these systems by experimentally using continuous-time methods or linearizing the node dynamics. In this manner, this paper presents a control scheme that not only deals with pin control on discrete-time complex networks but also includes time-varying connections. This paper proposes an impulsive pin control to a zero state using passivity degrees considering a discrete-time complex network with undirected, linear, and diffusive couplings. Additionally, a corresponding mathematical analysis, which allows the representation of the dynamics as a set of symmetric matrices, is presented. With this, certain kinds of time-varying connections can be integrated into the analysis. Moreover, a particular criterion for selecting nodes to pin is also presented. The behavior of the controller for the non-varying and time-varying coupling cases is shown via numeric simulations.

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16

Bai, Wei Gang, Hai Yan Wang, and Rui Qin Zhao. "Modeling Underwater Time-Varying Acoustic Channel Using OPNET." Applied Mechanics and Materials 263-266 (December 2012): 1178–83. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.1178.

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Underwater acoustic networks (UWAN) play a crucial role in the development of modern marine military defense and civilian marine. In many cases, the simulation of routing and MAC protocols in underwater acoustic network has ignored the impact of some critical features of complex underwater acoustic channel upon UWAN performances. This paper establishes a channel model for the Rayleigh fading channel in shallow-water medium-range communication in OPNET network simulation software. It simulates the time-varying and multi-path effects of underwater acoustic channel, which are reflected in the received power and bit error rate. Finally the validity of this channel model is verified by simulations.

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17

Zhang, Hebing, Xiaojing Zheng, and Ning Li. "Finite-Time Pinning Synchronization Control for Coupled Complex Networks with Time-Varying Delays." Discrete Dynamics in Nature and Society 2022 (May 18, 2022): 1–11. http://dx.doi.org/10.1155/2022/7119370.

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The finite-time pinning synchronization control problem is studied for coupled complex networks with time-varying delays. Based on the finite-time stability theorem, a finite-time tractive synchronous controller is designed. In addition, the selection process of tractive nodes is developed to control as few nodes as possible such that all nodes are synchronized in the network in finite time. At the same time, sufficient conditions of the finite-time constraint synchronization of the drive-response network are obtained using the Lyapunov stability theory and the matrix inequality method. The effectiveness of the proposed controller is verified by numerical simulation. This approach can be applied to large-scale complex networks with time-varying delays.

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18

Marjai, Péter, and Attila Kiss. "Efficiency centrality in time-varying graphs." Acta Universitatis Sapientiae, Informatica 12, no. 1 (July 1, 2020): 5–21. http://dx.doi.org/10.2478/ausi-2020-0001.

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AbstractOne of the most studied aspect of complex graphs is identifying the most influential nodes. There are some local metrics like degree centrality, which is cost-effiective and easy to calculate, although using global metrics like betweenness centrality or closeness centrality can identify influential nodes more accurately, however calculating these values can be costly and each measure has it’s own limitations and disadvantages. There is an ever-growing interest in calculating such metrics in time-varying graphs (TVGs), since modern complex networks can be best modelled with such graphs. In this paper we are investigating the effectiveness of a new centrality measure called efficiency centrality in TVGs. To evaluate the performance of the algorithm Independent Cascade Model is used to simulate infection spreading in four real networks. To simulate the changes in the network we are deleting and adding nodes based on their degree centrality. We are investigating the Time-Constrained Coverage and the magnitude of propagation resulted by the use of the algorithm.

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19

Hai, Long, Jie Wang, Ping Wang, Hongyu Wang, and Tingting Yang. "High-Throughput Network Coding Aware Routing in Time-Varying Multihop Networks." IEEE Transactions on Vehicular Technology 66, no. 7 (July 2017): 6299–309. http://dx.doi.org/10.1109/tvt.2016.2640313.

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20

Ding, Li, and Ping Hu. "Contagion Processes on Time-Varying Networks with Homophily-Driven Group Interactions." Complexity 2019 (October 30, 2019): 1–13. http://dx.doi.org/10.1155/2019/7130468.

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The complicated interaction patterns among heterogeneous individuals have a profound impact on the contagion process in the networks. In recent years, there has been increasing evidence for the emergence of many-body interactions between two or more nodes in a wide range of biological and social networks. To encode these multinode interactions explicitly, the simplicial complex is now a popular alternative to simple networks. Meanwhile, the time-varying network has been acknowledged as a key ingredient of the contagion process. In this paper, we consider the connectivity pattern of networks affected by the homophily effect associated with individual attributes and investigate the impact of homophily-driven group interactions on the contagion process in temporal networks. The simplicial complex modeling framework is adopted to capture stochastic interactions between passively selected nodes in the paradigm of activity-driven networks. We study the evolution of infection and the epidemic threshold of the contagion process by both analytical and numerical methods. Our results on statistical topological properties of instantaneous network may shed light on accurately characterizing the evolution curve of infection. Furthermore, we show the impact of the homophily-driven interaction pattern on the epidemic threshold, which generalizes the existing results on both the paradigmatic activity-driven network and the simplicial activity-driven network.

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21

Amgain, Dipak Babu, and Tanka Nath Dhamala. "Quickest Flow Algorithms with Time-Varying Attributes." Journal of Institute of Science and Technology 26, no. 1 (June 17, 2021): 63–73. http://dx.doi.org/10.3126/jist.v26i1.37826.

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In many real-world situations, there are numerous network optimization problems where the network attributes depend on time. In this paper, we consider single-source single-sink discrete-time dynamic network flow problems. We review some algorithms for the quickest flow problems in two environments (to the network attributes): time-invariant and time-variant. This paper mainly focuses on the existing algorithms for a later one. In literature, most of the authors have made their objectives to determine the earliest arrival time paths along which a given amount of flow can be sent in the minimum time. Evacuation is the most recent research area of network optimization, where quickest flow models allow the estimation of the minimum time required to bring a given number of evacuees to safety.

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22

Yi, Hyun-Chul, Cheol-Jin An, and Joon-Young Choi. "Compensation of Time-Varying Delay in Networked Control System over Wi-Fi Network." International Journal of Computers Communications & Control 12, no. 3 (April 23, 2017): 415. http://dx.doi.org/10.15837/ijccc.2017.3.2617.

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In this study, we design a state predictor-based output feedback controller that compensates for unavoidable time-varying network delays in networked control systems (NCSs) over Wi-Fi networks. We model time-varying network delays as timevarying input delays of NCSs over Wi-Fi networks. The designed controller consists of a linear quadratic regulator (LQR), a full-order observer, and a time-varying stepahead state predictor. The state predictor plays a key role in compensating for the time-varying input delay by providing the LQR with an estimation of future states ahead by the current network delay time. The time-varying network delays are acquired in real time by measuring the time differences between sent and received control data packets. We verify the stability and compensation performance of the designed controller by performing extensive experiments for an NCS in which a rotary inverted pendulum is controlled over Wi-Fi networks.

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23

RAGOZINI, GIANCARLO, DOMENICO DE STEFANO, and MARIA ROSARIA D'ESPOSITO. "Multiple factor analysis for time-varying two-mode networks." Network Science 3, no. 1 (February 12, 2015): 18–36. http://dx.doi.org/10.1017/nws.2015.5.

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AbstractMost social networks present complex structures. They can be both multi-modal and multi-relational. In addition, each relationship can be observed across time occasions. Relational data observed in such conditions can be organized into multidimensional arrays and statistical methods from the theory of multiway data analysis may be exploited to reveal the underlying data structure. In this paper, we adopt an exploratory data analysis point of view, and we present a procedure based on multiple factor analysis and multiple correspondence analysis to deal with time-varying two-mode networks. This procedure allows us to create static displays in order to explore network evolutions and to visually analyze the degree of similarity of actor/event network profiles over time while preserving the different statuses of the two modes.

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24

Carey, Malachy, and Ashok Srinivasan. "Congested network flows: Time-varying demands and start-time policies." European Journal of Operational Research 36, no. 2 (August 1988): 227–40. http://dx.doi.org/10.1016/0377-2217(88)90429-8.

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25

Wang, Gang-Jin, Chi Xie, Peng Zhang, Feng Han, and Shou Chen. "Dynamics of Foreign Exchange Networks: A Time-Varying Copula Approach." Discrete Dynamics in Nature and Society 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/170921.

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Based on a time-varying copula approach and the minimum spanning tree (MST) method, we propose a time-varying correlation network-based approach to investigate dynamics of foreign exchange (FX) networks. In piratical terms, we choose the daily FX rates of 42 major currencies in the international FX market during the period of 2005–2012 as the empirical data. The empirical results show that (i) the distributions of cross-correlation coefficients (distances) in the international FX market (network) are fat-tailed and negatively skewed; (ii) financial crises during the analyzed period have a great effect on the FX network’s topology structure and lead to the US dollar becoming more centered in the MST; (iii) the topological measures of the FX network show a large fluctuation and display long-range correlations; (iv) the FX network has a long-term memory effect and presents a scale-free behavior in the most of time; and (v) a great majority of links between currencies in the international FX market survive from one time to the next, and multistep survive rates of FX networks drop sharply as the time increases.

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26

Lü, Qingguo, and Huaqing Li. "Event-Triggered Discrete-Time Distributed Consensus Optimization over Time-Varying Graphs." Complexity 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/5385708.

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This paper focuses on a class of event-triggered discrete-time distributed consensus optimization algorithms, with a set of agents whose communication topology is depicted by a sequence of time-varying networks. The communication process is steered by independent trigger conditions observed by agents and is decentralized and just rests with each agent’s own state. At each time, each agent only has access to its privately local Lipschitz convex objective function. At the next time step, every agent updates its state by applying its own objective function and the information sent from its neighboring agents. Under the assumption that the network topology is uniformly strongly connected and weight-balanced, the novel event-triggered distributed subgradient algorithm is capable of steering the whole network of agents asymptotically converging to an optimal solution of the convex optimization problem. Finally, a simulation example is given to validate effectiveness of the introduced algorithm and demonstrate feasibility of the theoretical analysis.

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27

Nakajima, Natsu, and Tatsuya Akutsu. "Exact and Heuristic Methods for Network Completion for Time-Varying Genetic Networks." BioMed Research International 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/684014.

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Robustness in biological networks can be regarded as an important feature of living systems. A system maintains its functions against internal and external perturbations, leading to topological changes in the network with varying delays. To understand the flexibility of biological networks, we propose a novel approach to analyze time-dependent networks, based on the framework of network completion, which aims to make the minimum amount of modifications to a given network so that the resulting network is most consistent with the observed data. We have developed a novel network completion method for time-varying networks by extending our previous method for the completion of stationary networks. In particular, we introduce a double dynamic programming technique to identify change time points and required modifications. Although this extended method allows us to guarantee the optimality of the solution, this method has relatively low computational efficiency. In order to resolve this difficulty, we developed a heuristic method for speeding up the calculation of minimum least squares errors. We demonstrate the effectiveness of our proposed methods through computational experiments using synthetic data and real microarray gene expression data. The results indicate that our methods exhibit good performance in terms of completing and inferring gene association networks with time-varying structures.

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28

Zhang, Wei, Hong Ma, Tao Wu, Xueshu Shi, and Yiwen Jiao. "Efficient topology control for time-varying spacecraft networks with unreliable links." International Journal of Distributed Sensor Networks 15, no. 9 (September 2019): 155014771987937. http://dx.doi.org/10.1177/1550147719879377.

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In spacecraft networks, the time-varying topology, intermittent connectivity, and unreliable links make management of the network challenging. Previous works mainly focus on information propagation or routing. However, with a large number of nodes in the future spacecraft networks, it is very crucial regarding how to make efficient network topology controls. In this article, we investigate the topology control problem in spacecraft networks where the time-varying topology can be predicted. We first develop a model that formalizes the time-varying spacecraft network topologies as a directed space–time graph. Compared with most existing static graph models, this model includes both temporal and spatial topology information. To capture the characteristics of practical network, links in our space–time graph model are weighted by cost, efficiency, and unreliability. The purpose of our topology control is to construct a sparse (low total cost) structure from the original topology such that (1) the topology is still connected over space–time graph; (2) the cost efficiency ratio of the topology is minimized; and (3) the unreliability parameter is lower than the required bound. We prove that such an optimization problem is NP-hard. Then, we provide five heuristic algorithms, which can significantly maintain low topology cost efficiency ratio while achieving high reliable connectivity. Finally, simulations have been conducted on random space networks and hybrid low earth orbit/geostationary earth orbit satellite-based sensor network. Simulation results demonstrate the efficiency of our model and topology control algorithms.

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29

Zhang, Youjian, Wenjun Yan, and Qiang Yang. "Synchronization Control of Time-Varying Complex Dynamic Network with Nonidentical Nodes and Coupling Time-Delay." Mathematical Problems in Engineering 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/461635.

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This paper addresses the synchronization problem for a class of complex networks with time-varying topology as well as nonidentical nodes and coupling time-delay and presents two efficient control schemes to synchronize the network onto any given smooth goal dynamics. The time-varying network is supposed to be bounded within a certain range, which cannot be controlled. Through the adoption of hybrid control with linear static feedback control and adaptive feedback control, two control schemes are derived to guarantee such complex networks to reach the global synchronization. Finally, a set of numerical simulation experiments are carried out and the results demonstrate the effectiveness of the suggested control solutions.

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30

Majhi, Soumen, Sarbendu Rakshit, and Dibakar Ghosh. "Oscillation suppression and chimera states in time-varying networks." Chaos: An Interdisciplinary Journal of Nonlinear Science 32, no. 4 (April 2022): 042101. http://dx.doi.org/10.1063/5.0087291.

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Complex network theory has offered a powerful platform for the study of several natural dynamic scenarios, based on the synergy between the interaction topology and the dynamics of its constituents. With research in network theory being developed so fast, it has become extremely necessary to move from simple network topologies to more sophisticated and realistic descriptions of the connectivity patterns. In this context, there is a significant amount of recent works that have emerged with enormous evidence establishing the time-varying nature of the connections among the constituents in a large number of physical, biological, and social systems. The recent review article by Ghosh et al. [Phys. Rep. 949, 1–63 (2022)] demonstrates the significance of the analysis of collective dynamics arising in temporal networks. Specifically, the authors put forward a detailed excerpt of results on the origin and stability of synchronization in time-varying networked systems. However, among the complex collective dynamical behaviors, the study of the phenomenon of oscillation suppression and that of other diverse aspects of synchronization are also considered to be central to our perception of the dynamical processes over networks. Through this review, we discuss the principal findings from the research studies dedicated to the exploration of the two collective states, namely, oscillation suppression and chimera on top of time-varying networks of both static and mobile nodes. We delineate how temporality in interactions can suppress oscillation and induce chimeric patterns in networked dynamical systems, from effective analytical approaches to computational aspects, which is described while addressing these two phenomena. We further sketch promising directions for future research on these emerging collective behaviors in time-varying networks.

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31

Wang, Xiong, Jinhu Lu, and Xiaoqun Wu. "Recovering Network Structures With Time-Varying Nodal Parameters." IEEE Transactions on Systems, Man, and Cybernetics: Systems 50, no. 7 (July 2020): 2588–98. http://dx.doi.org/10.1109/tsmc.2018.2822780.

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CHEN, HaoYong, Ming LI, Ming QIU, HaiLin GE, and Long HUANG. "Modeling and analysis of time-varying energy network." SCIENTIA SINICA Technologica 49, no. 3 (November 27, 2018): 243–54. http://dx.doi.org/10.1360/n092018-00079.

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33

Wang, Gang, Huaguang Zhang, Bing Chen, and Shaocheng Tong. "Fuzzy hyperbolic neural network with time-varying delays." Fuzzy Sets and Systems 161, no. 19 (October 2010): 2533–51. http://dx.doi.org/10.1016/j.fss.2010.05.008.

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34

Huang, Dong Ming, and Lei Sun. "Time-Varying Delay Global Stability of Neural Networks." Applied Mechanics and Materials 65 (June 2011): 9–12. http://dx.doi.org/10.4028/www.scientific.net/amm.65.9.

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Hopfield neural networks with variable delay stability of the equilibrium point, the delayed neural network analysis of exponential convergence rate and exponential stability. Obtained by using Lyapunov functional stability of the index to determine the conditions, the use of a number of analytical methods to study the connection weight matrix and activation function of the boundary, has been the result of system is exponentially stable, and a numerical example to prove that the method effectiveness.

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35

Zhao, Hong. "Stability of Discrete-Time Network Control Systems with Time-Varying Delays." Advanced Materials Research 971-973 (June 2014): 1334–37. http://dx.doi.org/10.4028/www.scientific.net/amr.971-973.1334.

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This paper proposes delay-dependent stability conditions of discrete-time networked control systems (NCSs) with unknown, time-varying and bounded delays using Lyapunov-Krasovskii functional and the improved free-weighting matrix approach. Moreover, the conditions are extended to the NCSs with time-varying structured uncertainties.

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36

Fang, Xinli, Qiang Yang, and Wenjun Yan. "Outer Synchronization between Complex Networks with Nonlinear Time-Delay Characteristics and Time-Varying Topological Structures." Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/437673.

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This paper exploits the network outer synchronization problem in a generic context for complex networks with nonlinear time-delay characteristics and nonidentical time-varying topological structures. Based on the classic Lyapunov stability theory, the synchronization criteria and adaptive control strategy are presented, respectively, by adopting an appropriate Lyapunov-Krasovskii energy function and the convergence of the system error can also be well proved. The existing results of network outer synchronization can be obtained by giving certain conditions, for example, treating the coupling matrices as time-invariant, and by applying the suggested generic synchronization criteria and control scheme. The numerical simulation experiments for networks scenarios with dynamic chaotic characteristics and time-varying topologies are carried out and the result verifies the correctness and effectiveness of the proposed control solution.

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37

Li, Fali, Wenjing Peng, Yuanling Jiang, Limeng Song, Yuanyuan Liao, Chanlin Yi, Luyan Zhang, et al. "The Dynamic Brain Networks of Motor Imagery: Time-Varying Causality Analysis of Scalp EEG." International Journal of Neural Systems 29, no. 01 (January 10, 2019): 1850016. http://dx.doi.org/10.1142/s0129065718500168.

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Motor imagery (MI) requires subjects to visualize the requested motor behaviors, which involves a large-scale network that spans multiple brain areas. The corresponding cortical activity reflected on the scalp is characterized by event-related desynchronization (ERD) and then by event-related synchronization (ERS). However, the network mechanisms that account for the dynamic information processing of MI during the ERD and ERS periods remain unknown. Here, we combined ERD/ERS analysis with the dynamic networks in different MI stages (i.e. motor preparation, ERD and ERS) to probe the dynamic processing of MI information. Our results show that specific dynamic network structures correspond to the ERD/ERS evolution patterns. Specifically, ERD mainly shows the contralateral networks, while ERS has the symmetric networks. Moreover, different dynamic network patterns are also revealed between the two types of MIs, in which the left-hand MIs exhibit a relatively less sustained contralateral network, which may be the network mechanism that accounts for the bilateral ERD/ERS observed for the left-hand MIs. Similar to the network topologies, the three MI stages also appear to be characterized by different network properties. The above findings all demonstrate that different MI stages that involve specific brain networks for dynamically processing the MI information.

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38

YAN, JUN-JUH, TEH-LU LIAO, JUI-SHENG LIN, and CHAO-JUNG CHENG. "SYNCHRONIZATION CONTROL OF NEURAL NETWORKS SUBJECT TO TIME-VARYING DELAYS AND INPUT NONLINEARITY." International Journal of Bifurcation and Chaos 16, no. 12 (December 2006): 3643–54. http://dx.doi.org/10.1142/s0218127406017038.

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This paper investigates the synchronization problem for a particular class of neural networks subject to time-varying delays and input nonlinearity. Using the variable structure control technique, a memoryless decentralized control law is established which guarantees exponential synchronization even when input nonlinearity is present. The proposed controller is suitable for application in delayed cellular neural networks and Hopfield neural networks with no restriction on the derivative of the time-varying delays. A two-dimensional cellular neural network and a four-dimensional Hopfield neural network, both with time-varying delays, are presented as illustrative examples to demonstrate the effectiveness of the proposed synchronization scheme.

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39

Liu, Huanlin, Meng Wen, Yong Chen, Chang Tang, Junling Hu, and Haonan Chen. "Virtual optical network embedding of time-varying traffic in elastic optical networks." Optics Communications 508 (April 2022): 127693. http://dx.doi.org/10.1016/j.optcom.2021.127693.

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40

Liu, Lan, Ryan K. L. Ko, Guangming Ren, and Xiaoping Xu. "Malware Propagation and Prevention Model for Time-Varying Community Networks within Software Defined Networks." Security and Communication Networks 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/2910310.

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As the adoption of Software Defined Networks (SDNs) grows, the security of SDN still has several unaddressed limitations. A key network security research area is in the study of malware propagation across the SDN-enabled networks. To analyze the spreading processes of network malware (e.g., viruses) in SDN, we propose a dynamic model with a time-varying community network, inspired by research models on the spread of epidemics in complex networks across communities. We assume subnets of the network as communities and links that are dense in subnets but sparse between subnets. Using numerical simulation and theoretical analysis, we find that the efficiency of network malware propagation in this model depends on the mobility rate q of the nodes between subnets. We also find that there exists a mobility rate threshold qc. The network malware will spread in the SDN when the mobility rate q>qc. The malware will survive when q>qc and perish when q<qc. The results showed that our model is effective, and the results may help to decide the SDN control strategy to defend against network malware and provide a theoretical basis to reduce and prevent network security incidents.

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41

Mo, Changxin, Dimitrios Gerontitis, and Predrag S. Stanimirović. "Solving the time-varying tensor square root equation by varying-parameters finite-time Zhang neural network." Neurocomputing 445 (July 2021): 309–25. http://dx.doi.org/10.1016/j.neucom.2021.03.011.

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42

Zhang, Zhijun, Wenwei Lin, Lunan Zheng, Pengchao Zhang, Xilong Qu, and Yue Feng. "A power-type varying gain discrete-time recurrent neural network for solving time-varying linear system." Neurocomputing 388 (May 2020): 24–33. http://dx.doi.org/10.1016/j.neucom.2020.01.027.

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43

Guo, Dongsheng, Zhuoyun Nie, and Laicheng Yan. "Novel Discrete-Time Zhang Neural Network for Time-Varying Matrix Inversion." IEEE Transactions on Systems, Man, and Cybernetics: Systems 47, no. 8 (August 2017): 2301–10. http://dx.doi.org/10.1109/tsmc.2017.2656941.

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44

Zhang, Baowen, Kaiwen Jiang, and Wei Huang. "A Novel Maximum Flow Algorithm with Neural Network for Time-Varying Wastage Networks." Security and Communication Networks 2022 (September 30, 2022): 1–9. http://dx.doi.org/10.1155/2022/3782761.

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This paper introduces a time-varying wastage maximum flow problem (TWMFP) and proposes a time-flow neural network (TFNN) for solving the TWMFPs. The time-varying wastage maximum flow problem is concerned with finding the maximum flow in a network with time-varying arc capacities and additive flow losses on the arcs. This problem has multiple applications in transportation, communication, and financial network. For example, solving the maximum traffic flow of the transportation network and the maximum profit of the financial network. Unlike traditional neural network algorithms, the proposed TFNN does not require any training by means of its time-flow mechanism. The time-flow mechanism is realized by each active neuron sending pulses to its successor neurons. In order to maximize the network flow, the proposed TFNN can be divided into two parts: path-pulse neural networks (PPNNs) and subnet-flow neural networks (SFNN). PPNN is to generate two subnet sets (viz. with wastage arcs and without), and SFNN is to find the maximum flow value of each subnet. The subnet computing strategy of the proposed algorithm greatly improves the solution accuracy of TWMFPs. Theoretical analysis and experiments have proved the effectiveness of TFNN. The experiment results of the transportation network (viz. New York Road) show that the proposed TFNN has better performance (viz. error rate and computational time) compared to classical algorithms.

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45

Wei, Qiang, and Cheng-jun Xie. "Synchronization of mutual time-varying delay-coupled temporal Boolean networks." Measurement and Control 53, no. 7-8 (August 2020): 1504–11. http://dx.doi.org/10.1177/0020294020944951.

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This paper presents mutual time-varying delay-coupled temporal Boolean network model and investigates synchronization issue for mutual time-varying delay-coupled temporal Boolean networks. The necessary and sufficient conditions for the synchronization are given, and the check criterion of the upper bound is presented. An example is given to illustrate the correctness of the theoretical analysis.

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46

Gan, Liangzhi, Shuo Li, Na Duan, and Xiangyong Kong. "Adaptive Output Synchronization of General Complex Dynamical Network with Time-Varying Delays." Mathematics 8, no. 3 (March 1, 2020): 311. http://dx.doi.org/10.3390/math8030311.

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This paper is concerned with the output synchronization problems for a class of delayed complex dynamical networks. Based on the invariant principle of functional differential equations and Lyapunov stability theory, the feedback controller and parameter update laws are constructed for a large-scale network with uncertainties. The general complex delayed network can achieve synchronization by adaptively adjusting their feedback gains. Numerical examples are presented to further verify the effectiveness of the proposed control scheme.

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47

Calafiore, Giuseppe, and Fabrizio Abrate. "Distributed Maximum Likelihood Estimation with Time-Varying Network Topology." IFAC Proceedings Volumes 41, no. 2 (2008): 2850–55. http://dx.doi.org/10.3182/20080706-5-kr-1001.00480.

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48

Rao, Arvind, Alfred O. Hero III, David J. States, and James Douglas Engel. "Inferring Time-Varying Network Topologies from Gene Expression Data." EURASIP Journal on Bioinformatics and Systems Biology 2007 (2007): 1–12. http://dx.doi.org/10.1155/2007/51947.

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49

Donq-Liang Lee. "Pattern sequence recognition using a time-varying Hopfield network." IEEE Transactions on Neural Networks 13, no. 2 (March 2002): 330–42. http://dx.doi.org/10.1109/72.991419.

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50

Chen, Junting, Vincent K. N. Lau, and Yong Cheng. "Distributive Network Utility Maximization Over Time-Varying Fading Channels." IEEE Transactions on Signal Processing 59, no. 5 (May 2011): 2395–404. http://dx.doi.org/10.1109/tsp.2011.2106124.

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