Journal articles on the topic 'Network Dynamics Simulation'
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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 (June 20, 2013): 125–37. http://dx.doi.org/10.33225/pec/13.54.125.
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The present study is an analysis of the learning activity, which constitutes simulation of networks and studying their functioning and dynamics. The study is based on using network-like learning environments. Such environments allow building computer models of the network graphs. According to the suggested approach, the students construct dynamic computer models of the networks' graphs, thus implementing various algorithms of such networks’ dynamics. The suggested tool for building the models is the software environment comprising network analysis software NodeXL and a standard spreadsheet Excel. The proposed approach enables the students to visualize the network's dynamics. The paper presents specific examples of network models and various algorithms of the network's dynamics, which were developed based on the proposed approach. Key words: learning environments, modelling, social networks.
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MENDES, R. VILELA. "TOOLS FOR NETWORK DYNAMICS." International Journal of Bifurcation and Chaos 15, no. 04 (April 2005): 1185–213. http://dx.doi.org/10.1142/s0218127405012715.
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Networks have been studied mainly by statistical methods which emphasize their topological structure. Here, one collects some mathematical tools and results which might be useful to study both the dynamics of agents living on the network and the networks themselves as evolving dynamical systems. They include decomposition of differential dynamics, ergodic techniques, estimates of invariant measures, construction of non deterministic automata, logical approaches, etc. A few network examples are discussed as an application of the dynamical tools.
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Zhu, Zhiqiang. "Control Analysis of Propagation Dynamics on Networks." Journal of Physics: Conference Series 2224, no. 1 (April 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 information in the network by controlling the location and release time of intervention information.
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Kiss, Istvan Z., Luc Berthouze, Timothy J. Taylor, and Péter L. Simon. "Modelling approaches for simple dynamic networks and applications to disease transmission models." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 468, no. 2141 (January 18, 2012): 1332–55. http://dx.doi.org/10.1098/rspa.2011.0349.
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In this paper a random link activation–deletion (RLAD) model is proposed that gives rise to a stochastically evolving network. This dynamic network is then coupled to a simple susceptible-infectious-suceptible ( SIS ) dynamics on the network, and the resulting spectrum of model behaviour is explored via simulation and a novel pairwise model for dynamic networks. First, the dynamic network model is systematically analysed by considering link-type independent and dependent network dynamics coupled with globally constrained link creation. This is done rigorously with some analytical results and we highlight where such analysis can be performed and how these simpler models provide a benchmark to test and validate full simulations. The pairwise model is used to study the interplay between SIS -type dynamics on the network and link-type-dependent activation–deletion. Assumptions of the pairwise model are identified and their implications interpreted in a way that complements our current understanding. Furthermore, we also discuss how the strong assumptions of the closure relations can lead to disagreement between the simulation and pairwise model. Unlike on a static network, the resulting spectrum of behaviour is more complex with the prevalence of infections exhibiting not only a single steady state, but also bistability and oscillations.
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Esser, J., and M. Schreckenberg. "Microscopic Simulation of Urban Traffic Based on Cellular Automata." International Journal of Modern Physics C 08, no. 05 (October 1997): 1025–36. http://dx.doi.org/10.1142/s0129183197000904.
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Saturated capacities in traffic systems evoke increasing interest in simulations of complex networks serving as laboratory environment for developing management strategies. Especially for urban areas questions concerning overall traffic control have to be considered with regard to their impacts on the whole network. Modeling traffic flow dynamics using cellular automata allows us to run large network traffic simulations with only comparatively low computational efforts. We present a traffic simulation tool for urban road networks which is based on the Nagel–Schreckenberg Model. Arbitrary kinds of roads and crossings are modeled as combinations of only a few basic elements. Furthermore parking capacities are considered as well as circulations of public transports. The vehicles are driven corresponding to route plans or at random depending on the available data. The application of this network simulation covers investigations on the field of traffic planning as well as online simulations based on real-time traffic data as basis for dynamic traffic management systems.
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SCHMIDT, G., G. ZAMORA-LÓPEZ, and J. KURTHS. "SIMULATION OF LARGE SCALE CORTICAL NETWORKS BY INDIVIDUAL NEURON DYNAMICS." International Journal of Bifurcation and Chaos 20, no. 03 (March 2010): 859–67. http://dx.doi.org/10.1142/s0218127410026149.
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Understanding the functional dynamics of the mammalian brain is one of the central aims of modern neuroscience. Mathematical modeling and computational simulations of neural networks can help in this quest. In recent publications, a multilevel model has been presented to simulate the resting-state dynamics of the cortico-cortical connectivity of the mammalian brain. In the present work we investigate how much of the dynamical behavior of the multilevel model can be reproduced by a strongly simplified model. We find that replacing each cortical area by a single Rulkov map recreates the patterns of dynamical correlation of the multilevel model, while the outcome of other models and setups mainly depends on the local network properties, e.g. the input degree of each vertex. In general, we find that a simple simulation whose dynamics depends on the global topology of the whole network is far from trivial. A systematic analysis of different dynamical models and coupling setups is required.
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Kadupitiya, JCS, Geoffrey C. Fox, and Vikram Jadhao. "Machine learning for parameter auto-tuning in molecular dynamics simulations: Efficient dynamics of ions near polarizable nanoparticles." International Journal of High Performance Computing Applications 34, no. 3 (January 14, 2020): 357–74. http://dx.doi.org/10.1177/1094342019899457.
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Simulating the dynamics of ions near polarizable nanoparticles (NPs) using coarse-grained models is extremely challenging due to the need to solve the Poisson equation at every simulation timestep. Recently, a molecular dynamics (MD) method based on a dynamical optimization framework bypassed this obstacle by representing the polarization charge density as virtual dynamic variables and evolving them in parallel with the physical dynamics of ions. We highlight the computational gains accessible with the integration of machine learning (ML) methods for parameter prediction in MD simulations by demonstrating how they were realized in MD simulations of ions near polarizable NPs. An artificial neural network–based regression model was integrated with MD simulation and predicted the optimal simulation timestep and optimization parameters characterizing the virtual system with 94.3% success. The ML-enabled auto-tuning of parameters generated accurate dynamics of ions for ≈ 10 million steps while improving the stability of the simulation by over an order of magnitude. The integration of ML-enhanced framework with hybrid Open Multi-Processing / Message Passing Interface (OpenMP/MPI) parallelization techniques reduced the computational time of simulating systems with thousands of ions and induced charges from thousands of hours to tens of hours, yielding a maximum speedup of ≈ 3 from ML-only acceleration and a maximum speedup of ≈ 600 from the combination of ML and parallel computing methods. Extraction of ionic structure in concentrated electrolytes near oil–water emulsions demonstrates the success of the method. The approach can be generalized to select optimal parameters in other MD applications and energy minimization problems.
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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 (May 2021): 2150080. http://dx.doi.org/10.1142/s0218127421500802.
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We consider complex networks where the dynamics of each interacting agent is given by a nonlinear vector field and the connections between the agents are defined according to the topology of undirected simple graphs. The aim of the work is to explore whether the asymptotic dynamic behavior of the entire network can be fully determined from the knowledge of the dynamic properties of the underlying constituent agents. While the complexity that arises by connecting many nonlinear systems hinders us to analytically determine general solutions, we show that there are conditions under which the dynamical properties of the constituent agents are equivalent to the dynamical properties of the entire network. This feature, which depends on the nature and structure of both the agents and connections, leads us to define the concept of regions of reduced dynamics, which are subsets of the parameter space where the asymptotic solutions of a network behave equivalently to the limit sets of the constituent agents. On one hand, we discuss the existence of regions of reduced dynamics, which can be proven in the case of diffusive networks of identical agents with all-to-all topologies and conjectured for other topologies. On the other hand, using three examples, we show how to locate regions of reduced dynamics in parameter space. In simple cases, this can be done analytically through bifurcation analysis and in other cases we exploit numerical continuation methods.
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Sugiki, Nao, Shogo Nagao, Fumitaka Kurauchi, Mustafa Mutahari, and Kojiro Matsuo. "Social Dynamics Simulation Using a Multi-Layer Network." Sustainability 13, no. 24 (December 13, 2021): 13744. http://dx.doi.org/10.3390/su132413744.
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The analysis and evaluation of urban structure are important while considering sustainable urban policies. It is necessary to develop a method that can easily analyze the social dynamics that are the result of changes over time in urban transportation and land use. Therefore, by describing the relationships between various agents in urban areas as a network, it is possible to analyze them by focusing on their structures. However, since there are few existing studies on social dynamics using network-based methods, it is necessary to examine the validity and effectiveness of these methods. The purpose of this study is to examine the possibility of urban analysis and evaluation focusing on the network shape by describing the urban activities and modeling the dynamics with a multilayer network. In particular, we focus on household composition and individual facility access, examine what kind of interpretation is possible for network indicators, and mention the applicability of complex networks to urban analysis. The model was applied to a two-dimensional grid virtual city, and the household composition and individual facility accessibility were quantified using the centrality index.
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Ng, Desmond. "The social dynamics of diverse and closed networks." Human Systems Management 23, no. 2 (June 3, 2004): 111–22. http://dx.doi.org/10.3233/hsm-2004-23206.
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Inherent to the dynamics of social networks is a paradoxical trade-off between closed networks that promote cooperation and efficiency and diverse networks that are flexible to new resources and ideas. Since actors cannot simultaneously maximize both facets of a network, this has created a sharp debate on the social capital performance of closed and diverse network relationships. Research on this social capital debate has often focused on these described network affects without explaining the origins and dynamics of network performance. This paper advances a cognitive diversity approach that is based upon the subjective and alert behaviors of Austrian entrepreneurs. These are key causal drivers to this paper's theoretical model of social dynamics and performance of closed and diverse networks. Such network behavior is subsequently modeled as a Complex Adaptive system. Using agent-based simulation, an agent-based model of entrepreneurship and social network dynamics is constructed to test the relationships described by the proposed theoretical model. The simulation results support the described hypothesized relationships. These findings also suggest the benefits of closed and diverse networks are logically distinct and, thus, should not be viewed as an either-or phenomenon. Agent-based simulation results show entrepreneurs can construct a balanced network of closed and diverse networks to optimize the benefits of both networks.
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Huang, Tousheng, Huayong Zhang, Shengnan Ma, Ge Pan, Zhaodeng Wang, and Hai Huang. "Bifurcations, Complex Behaviors, and Dynamic Transition in a Coupled Network of Discrete Predator-Prey System." Discrete Dynamics in Nature and Society 2019 (June 13, 2019): 1–22. http://dx.doi.org/10.1155/2019/2583730.
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The nonlinear dynamics of predator-prey systems coupled into network is an important issue in recent biological advances. In this research, we consider each node of the coupled network represents a discrete predator-prey system, and the network dynamics is investigated. By applying Jacobian matrix, center manifold theorem and bifurcation theorems, stability of fixed points, flip bifurcation and Neimark-Sacker bifurcation of the discrete predator-prey system are analyzed. Via the method of Lyapunov exponents, the nonchaos-chaos transition of the coupled network along the routes to chaos induced by bifurcations is determined. Numerical simulations are performed to demonstrate the bifurcations, various attractors and dynamic transitions of the coupled network. Via comparison, we find that the coupled network exhibits far richer and more complex behaviors than single predator-prey system, including period-doubling cascades in orbits of period-2, period-4, period-8, invariant closed curves, dynamic windows for periodic orbits and invariant curves, quasiperiodic orbits, tori, and chaotic sets. Moreover, the attractors of the coupled network show more diverse and complicated structures. These results may provide a new perspective on the predator-prey dynamics in complex networks.
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Kassem, Ayman Hamdy. "Efficient Neural Network Modeling for Flight and Space Dynamics Simulation." International Journal of Aerospace Engineering 2011 (2011): 1–7. http://dx.doi.org/10.1155/2011/247294.
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This paper represents an efficient technique for neural network modeling of flight and space dynamics simulation. The technique will free the neural network designer from guessing the size and structure for the required neural network model and will help to minimize the number of neurons. For linear flight/space dynamics systems, the technique can find the network weights and biases directly by solving a system of linear equations without the need for training. Nonlinear flight dynamic systems can be easily modeled by training its linearized models keeping the same network structure. The training is fast, as it uses the linear system knowledge to speed up the training process. The technique is tested on different flight/space dynamic models and showed promising results.
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LI, KE PING, and ZI YOU GAO. "NETWORK-BASED SIMULATION APPROACH FOR SCHEDULING TRAINS ON RAIL NETWORKS." International Journal of Modern Physics C 17, no. 09 (September 2006): 1349–58. http://dx.doi.org/10.1142/s0129183106009710.
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Simulations for the train schedule on a rail network is strongly characterized by the dynamics of train movement. In the past, several simulation approaches have been proposed in this aspect, however, they had not gotten a satisfactory result. This paper presents an approach with which a network model is considered for the simuleting the train schedule on rail networks. Here the stations and section tracks of the rail network are respectively regarded as the nodes and edges of the network model. Using the proposed model, we simulate the train schedule with double-track sections. The simulation results indicate that the proposed model can be successfully used for the simulations of the train schedule on a rail networks. Some phenomena observed in real rail networks can be reproduced, such as the characteristic behavior of train movement. Moreover, the proposed model can handle the perturbations in the train schedule well.
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Yang, Lixin, Jie Gao, and Jie Ma. "Modeling and Dynamical Analysis of Multi-Area Network with a Third-Order Chaotic Power System." Discrete Dynamics in Nature and Society 2021 (October 28, 2021): 1–10. http://dx.doi.org/10.1155/2021/8421754.
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The synchronization of states is important to sustain the energy of consumers at any given time for power networks. This paper focuses on the multi-area power network model and then analyzes the cluster synchronization of this kind of network comprised of a third-order chaotic power system. Specifically, we investigate the rich dynamic properties of the single third-order power system. Furthermore, the multi-area network model with the chaotic power system is proposed and the adaptive controller is designed to achieve cluster synchronization. Combining analytical considerations with numerical simulations on a small-scale network, we address the cluster synchronous performance in the multi-area power network. Therefore, our results can provide a basic physical picture for power system dynamics and enable us to further understand the complex dynamical behavior in the multi-area power network.
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Burghout, Wilco, Haris N. Koutsopoulos, and Ingmar Andréasson. "Hybrid Mesoscopic–Microscopic Traffic Simulation." Transportation Research Record: Journal of the Transportation Research Board 1934, no. 1 (January 2005): 218–25. http://dx.doi.org/10.1177/0361198105193400123.
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Traffic simulation is an important tool for modeling the operations of dynamic traffic systems. Although microscopic simulation models provide a detailed representation of the traffic process, macroscopic and mesoscopic models capture the traffic dynamics of large networks in less detail but without the problems of application and calibration of microscopic models. This paper presents a hybrid mesoscopic–microscopic model that applies microscopic simulation to areas of specific interest while simulating a large surrounding network in less detail with a mesoscopic model. The requirements that are important for a hybrid model to be consistent across the models at different levels of detail are identified. These requirements vary from the network and route choice consistency to the consistency of the traffic dynamics at the boundaries of the microscopic and mesoscopic submodels. An integration framework that satisfies these requirements is proposed. A prototype hybrid model is used to demonstrate the application of the integration framework and the solution of the various integration issues. The hybrid model integrates MITSIMLab, a microscopic traffic simulation model, and Mezzo, a newly developed mesoscopic model. The hybrid model is applied in two case studies. The results are promising and support both the proposed architecture and the importance of integrating microscopic and mesoscopic models.
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Salwinski, Lukasz, and David Eisenberg. "In silico simulation of biological network dynamics." Nature Biotechnology 22, no. 8 (July 4, 2004): 1017–19. http://dx.doi.org/10.1038/nbt991.
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Buttà, Paolo, Fiammetta Cerreti, Vito D. P. Servedio, and Livio Triolo. "Molecular dynamics simulation of vascular network formation." Journal of Statistical Mechanics: Theory and Experiment 2009, no. 05 (May 20, 2009): P05013. http://dx.doi.org/10.1088/1742-5468/2009/05/p05013.
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Masumoto, Y., and Y. Iida. "Investigation of the Microscopic Viscoelastic Property for Cross-linked Polymer Network by Molecular Dynamics Simulation." Tire Science and Technology 39, no. 1 (March 1, 2011): 44–58. http://dx.doi.org/10.2346/1.3555178.
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Abstract The purpose of this work is to develop a new analytical method for simulating the microscopic mechanical property of the cross-linked polymer system using the coarse-grained molecular dynamics simulation. This new analytical method will be utilized for the molecular designing of the tire rubber compound to improve the tire performances such as rolling resistance and wet traction. First, we evaluate the microscopic dynamic viscoelastic properties of the cross-linked polymer using coarse-grained molecular dynamics simulation. This simulation has been conducted by the coarse-grained molecular dynamics program in the OCTA) (http://octa.jp/). To simplify the problem, we employ the bead-spring model, in which a sequence of beads connected by springs denotes a polymer chain. The linear polymer chains that are cross-linked by the cross-linking agents express the three-dimensional cross-linked polymer network. In order to obtain the microscopic dynamic viscoelastic properties, oscillatory deformation is applied to the simulation cell. By applying the time-temperature reduction law to this simulation result, we can evaluate the dynamic viscoelastic properties in the wide deformational frequency range including the rubbery state. Then, the stress is separated into the nonbonding stress and the bonding stress. We confirm that the contribution of the nonbonding stress is larger at lower temperatures. On the other hand, the contribution of the bonding stress is larger at higher temperatures. Finally, analyzing a change of microscopic structure in dynamic oscillatory deformation, we determine that the temperature/frequency dependence of bond stress response to a dynamic oscillatory deformation depends on the temperature dependence of the average bond length in the equilibrium structure and the temperature/frequency dependence of bond orientation. We show that our simulation is a useful tool for studying the microscopic properties of a cross-linked polymer.
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Purchase, Sharon, Doina Olaru, and Terje I. Vaaland. "Consequences of Structural Changes within Facilitated Innovative Networks." International Journal of Entrepreneurship and Innovation 7, no. 3 (August 2006): 161–71. http://dx.doi.org/10.5367/000000006778026581.
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Changes to industrial business network structures have been examined via discrete simulation to investigate how network positions change. In dynamic networks, active actors/forces continuously modify the status of the network, and changes touch all actors in the network. Such changes highlight the role of network effects and how they flow throughout the network. In this research, network position is assessed through closeness centrality and information flow through distance and network density. Simulation is used to examine the changes in network position triggered by alteration of dyadic relationships. Results indicate that network actors need to consider both position and density when determining whether they are ‘better off’ under different scenarios. This research builds on the network dynamics research topic and provides a flexible approach for investigating quantitatively potential changes in industrial networks.
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Mattia, Maurizio, and Paolo Del Giudice. "Efficient Event-Driven Simulation of Large Networks of Spiking Neurons and Dynamical Synapses." Neural Computation 12, no. 10 (October 1, 2000): 2305–29. http://dx.doi.org/10.1162/089976600300014953.
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A simulation procedure is described for making feasible large-scale simulations of recurrent neural networks of spiking neurons and plastic synapses. The procedure is applicable if the dynamic variables of both neurons and synapses evolve deterministically between any two successive spikes. Spikes introduce jumps in these variables, and since spike trains are typically noisy, spikes introduce stochasticity into both dynamics. Since all events in the simulation are guided by the arrival of spikes, at neurons or synapses, we name this procedure event-driven. The procedure is described in detail, and its logic and performance are compared with conventional (synchronous) simulations. The main impact of the new approach is a drastic reduction of the computational load incurred upon introduction of dynamic synaptic efficacies, which vary organically as a function of the activities of the pre- and postsynaptic neurons. In fact, the computational load per neuron in the presence of the synaptic dynamics grows linearly with the number of neurons and is only about 6% more than the load with fixed synapses. Even the latter is handled quite efficiently by the algorithm. We illustrate the operation of the algorithm in a specific case with integrate-and-fire neurons and specific spike-driven synaptic dynamics. Both dynamical elements have been found to be naturally implementable in VLSI. This network is simulated to show the effects on the synaptic structure of the presentation of stimuli, as well as the stability of the generated matrix to the neural activity it induces.
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Kawa, Arkadiusz, and Konrad Fuks. "AN ANALYSIS OF INTER-ORGANIZATIONAL NETWORK DYNAMICS ON THE EXAMPLE OF ELECTRONIC FREIGHT EXCHANGE." Zeszyty Naukowe Uniwersytetu Gdańskiego. Ekonomika Transportu i Logistyka 68, no. 1 (October 17, 2017): 49–63. http://dx.doi.org/10.5604/01.3001.0010.5322.
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Inter-organizational networks are the subject of numerous research projects, which explore not only the importance of companies within a network, its impact on the companies, and the ties between the actors, but also the structure of networks. Due to the rapidly changing environment of organizations, the need arises for an analysis of the dynamics of networks, which can be performed using Dynamic Network Analysis (DNA). The aim of the paper is to propose the use of this method for the analysis of network ties occurring in electronic freight exchange. For this purpose, a model was developed and implemented in a simulation environment. On the basis of selected scenarios, a simulation experiment was carried out. The authors present the most important conclusions from the statistical analysis of the experiments.
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Eduardo Tarifa, Enrique, Eleonora Erdmann, Demetrio Humana, María Soledad Vicente, Luis Rodolfo Cari, and Lorgio Mercado Fuentes. "Gas transport network analysis." Ingeniería e Investigación 27, no. 3 (September 1, 2007): 89–97. http://dx.doi.org/10.15446/ing.investig.v27n3.14849.
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Growing demand for natural gas necessarily leads to demands for increased transport network capacity. This can be done by increasing the capacity of already installed gas pipelines and optimising operating conditions. Greater knowledge (know-how) regarding the process is thus needed and may be applied by following the procedure outlined in this work. The proposed method concerns studying a gas network by using simulation tools; it has been used for studying a transport network in Argentina. The proposed method has the following stages: 1) system analysis (identifying parameters, disturbances, manipulated variables, state variables and output variables), 2) stationary simulation, 3) dynamic simulation and 4) case studies (analysing sensitivity, stability and controllability). Once a system’s critical variables have been identified then stationary simulation allows the amount of gas and its pressure to be determined for each sink, in several scenarios. These results can be used for designing suitable operational procedure for such cases. Dynamic simulation describes a system’s stationary state and how the state of the process evolves. Such additional information allows refining previously-designed procedures and also makes dynamic simulation an excellent tool for operator training. Two alternatives were analysed for stationary simulation: an HYSYS simulator and traditional Excel spreadsheet calculations. Predicted stationary states were similar by both methods. The sensitivity of the most relevant system variables was then studied; the HYSYS simulator was used for dynamic simulation in all cases. System sensitivity and dynamics were determined, such information being required for making improvements to network installations and operational procedures and thereby proving the procedure’s worth.
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Hong, Sheng, Hongqi Yang, Guoqi Li, Ning Huang, Xiaomin Ma, and K. S. Trivedi. "Analysis of propagation dynamics in complex dynamical network based on disturbance propagation model." International Journal of Modern Physics B 28, no. 22 (July 3, 2014): 1450149. http://dx.doi.org/10.1142/s0217979214501495.
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The paper regards the complex dynamical network (CDN) as a static network with temporal characteristics so as to consider its dynamic behavior. The influence factor and dynamics laws in CDN are explored by using the methods of simulation and statistical physics. It is found that both the dynamic behavior of the nodes and the number of initial disturbed nodes can accelerate the failure propagation and enlarge the influence scope. There is a phase transformation point of the repair ratio with the increase of repair time τ for the homogeneous network (the value of τ for all the nodes is the same). Beyond the threshold, the entire network will collapse; otherwise, the network can be back to normal after a period of time. There is an inflection point of the repair rate with the simulation step and the recovery rate in heterogeneous network is much slower than that in homogeneous network. The results can provide technical support for the fault management and network optimization in CDN which will promote the development of corresponding theory in CDN.
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Bunimovich, Leonid, D. J. Passey, Dallas Smith, and Benjamin Webb. "Spectral and Dynamic Consequences of Network Specialization." International Journal of Bifurcation and Chaos 30, no. 06 (May 2020): 2050091. http://dx.doi.org/10.1142/s0218127420500911.
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One of the hallmarks of real networks is the ability to perform increasingly complex tasks as their topology evolves. To explain this, it has been observed that as a network grows certain subsets of the network begin to specialize the function(s) they perform. A recent model of network growth based on this notion of specialization has been able to reproduce some of the most well-known topological features found in real-world networks including right-skewed degree distributions, the small world property, modular as well as hierarchical topology, etc. Here we describe how specialization under this model also effects the spectral properties of a network. This allows us to give the conditions under which a network is able to maintain its dynamics as its topology evolves. Specifically, we show that if a network is intrinsically stable, which is a stronger version of the standard notion of global stability, then the network maintains this type of dynamics as the network evolves. This is one of the first steps toward unifying the rigorous study of the two types of dynamics exhibited by networks. These are the dynamics of a network, which is the topological evolution of the network’s structure, modeled here by the process of network specialization, and the dynamics on a network, which is the changing state of the network elements, where the type of dynamics we consider is global stability. The main examples we apply our results to are recurrent neural networks, which are the basis of certain types of machine learning algorithms.
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Ma, Jinlong, Zhichao Sun, Yongqiang Zhang, Xiangyang Xu, Ruimei Zhao, Mingwei Cai, and Sufeng Li. "Traffic dynamics on multilayer networks with two logical layers." International Journal of Modern Physics B 35, no. 07 (March 20, 2021): 2150109. http://dx.doi.org/10.1142/s0217979221501095.
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In order to study traffic dynamics on multilayer networks, it is of great significance to build a network model which can more exactly reflect the actual network layered structure characteristics. In this paper, a three-layer network model in which two logical layers are mapped on one physical layer is established, and the traffic capacities of three kinds of multilayer networks with different combinations of logical layers are compared. Simulation results show that when the physical layer is the same random network, the network whose logical layers are two random networks has the optimal traffic capacity, the network with one random network and one scale-free network in the logical layers has the better traffic capacity than the network whose logical layers are two scale-free networks.
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Xiao, Yunpeng, Bai Wang, Bin Wu, Zhixian Yan, Shousheng Jia, and Yanbing Liu. "A Hybrid Human Dynamics Model on Analyzing Hotspots in Social Networks." Discrete Dynamics in Nature and Society 2012 (2012): 1–13. http://dx.doi.org/10.1155/2012/678286.
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The increasing development of social networks provides a unique source for analyzing human dynamics in the modern age. In this paper, we analyze the top-one Internet forum in China (“Tianya Club”) and identify the statistical properties of hotspots, which can promptly reflect the crowd events in people's real-life. Empirical observations indicate that the interhotspot distribution follows a power law. To further understand the mechanism of such dynamic phenomena, we propose a hybrid human dynamic model that combines “memory” of individual and “interaction” among people. To build a rich simulation and evaluate this hybrid model, we apply three different network datasets (i.e., WS network, BA network, and Karate-Club). Our simulation results are consistent with the empirical studies, which indicate that the model can provide a good understanding of the dynamic mechanism of crowd events using such social networking data. We additionally analyze the sensitivity of model parameters and find the optimal model settings.
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AGUILAR-HIDALGO, DANIEL, ANTONIO CÓRDOBA ZURITA, and Ma CARMEN LEMOS FERNÁNDEZ. "COMPLEX NETWORKS EVOLUTIONARY DYNAMICS USING GENETIC ALGORITHMS." International Journal of Bifurcation and Chaos 22, no. 07 (July 2012): 1250156. http://dx.doi.org/10.1142/s0218127412501568.
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Gene regulatory networks set a second order approximation to genetics understanding, where the first order is the knowledge at the single gene activity level. With the increasing number of sequenced genomes, including humans, the time has come to investigate the interactions among myriads of genes that result in complex behaviors. These characteristics are included in the novel discipline of Systems Biology. The composition and unfolding of interactions among genes determine the activity of cells and, when is considered during development, the organogenesis. Hence the interest of building representative networks of gene expression and their time evolution, i.e. the structure as the network dynamics, for certain development processes. The complexity of this kind of problems makes imperative to analyze the problem in the field of network theory and the evolutionary dynamics of complex systems.All this has led us to investigate, in a first step, the evolutionary dynamics in generic networks. Thus, the results can be used in experimental researches in the field of Systems Biology. This research aims to decode the transformation rules governing the evolutionary dynamics in a network. To do this, a genetic algorithm has been implemented in which, starting from initial and ending network states, it is possible to determine the transformation dynamics between these states by using simple acting rules. The network description is the following: (a) The network node values in the initial and ending states can be active or inactive; (b) The network links can act as activators or repressors; (c) A set of rules is established in order to transform the initial state into the ending one; (d) Due to the low connectivity, frequently observed, in gene regulatory networks, each node will hold a maximum of three inputs with no restriction on outputs. The "chromosomes" of the genetic algorithm include two parts, one related to the node links and another related to the transformation rules.The implemented rules are based on certain genetic interactions behavior. The rules and their combinations are compound by logic conditions and set the bases to the network motifs formation, which are the building blocks of the network dynamics.The implemented algorithm is able to find appropriate dynamics in complex networks evolution among different states for several cases.
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Wu, Liangfang, Shiyang Liu, and Huimin Zhang. "Opinion dynamics in typical networks with local and global social impacts together." International Journal of Modern Physics C 31, no. 08 (July 16, 2020): 2050110. http://dx.doi.org/10.1142/s0129183120501107.
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We introduce a new opinion model to study the opinion evolving in three typical networks (small-world network, scale-free network and highly clustered scale-free network) by employing Monte Carlo numerical simulation. To consider important social influence, we propose a heat bath like optimized opinion model that includes both local and global social impacts. By employing the new opinion model into three typical networks, we have found the main determinants of the evolution of the opinion, that were the weight constant of the local and global effects, the structure of the network, the size of the network and the initial density of the average opinion. The simulations show how those factors affect the evolving time of reaching the consensus quantitatively. Our model also could explain the cases of de facto standard and lock-in effect, well-known phenomenon in economics and business management.
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Albers, D. J., J. C. Sprott, and W. D. Dechert. "Routes to Chaos in Neural Networks with Random Weights." International Journal of Bifurcation and Chaos 08, no. 07 (July 1998): 1463–78. http://dx.doi.org/10.1142/s0218127498001121.
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Neural networks are dense in the space of dynamical system. We present a Monte Carlo study of the dynamic properties along the route to chaos over random dynamical system function space by randomly sampling the neural network function space. Our results show that as the dimension of the system (the number of dynamical variables) is increased, the probability of chaos approaches unity. We present theoretical and numerical results which show that as the dimension is increased, the quasiperiodic route to chaos is the dominant route. We also qualitatively analyze the dynamics along the route.
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TSOUMANIS, A. C., C. I. SIETTOS, G. V. BAFAS, and I. G. KEVREKIDIS. "EQUATION-FREE MULTISCALE COMPUTATIONS IN SOCIAL NETWORKS: FROM AGENT-BASED MODELING TO COARSE-GRAINED STABILITY AND BIFURCATION ANALYSIS." International Journal of Bifurcation and Chaos 20, no. 11 (November 2010): 3673–88. http://dx.doi.org/10.1142/s0218127410027945.
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We focus on the "trijunction" between multiscale computations, bifurcation theory and social networks. In particular, we address how the Equation-Free approach, a recently developed computational framework, can be exploited to systematically extract coarse-grained, emergent dynamical information by bridging detailed, agent-based models of social interactions on networks, with macroscopic, systems-level, continuum numerical analysis tools. For our illustrations, we use a simple dynamic agent-based model describing the propagation of information between individuals interacting under mimesis in a social network with private and public information. We describe the rules governing the evolution of the agents' emotional state dynamics and discover, through simulation, multiple stable stationary states as a function of the network topology. Using the Equation-Free approach we track the dependence of these stationary solutions on network parameters and quantify their stability in the form of coarse-grained bifurcation diagrams.
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Qiu, Rong, Yujiao Dong, Xin Jiang, and Guangyi Wang. "Two-Neuron Based Memristive Hopfield Neural Network with Synaptic Crosstalk." Electronics 11, no. 19 (September 23, 2022): 3034. http://dx.doi.org/10.3390/electronics11193034.
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Synaptic crosstalk is an important biological phenomenon that widely exists in neural networks. The crosstalk can influence the ability of neurons to control the synaptic weights, thereby causing rich dynamics of neural networks. Based on the crosstalk between synapses, this paper presents a novel two-neuron based memristive Hopfield neural network with a hyperbolic memristor emulating synaptic crosstalk. The dynamics of the neural networks with varying memristive parameters and crosstalk weights are analyzed via the phase portraits, time-domain waveforms, bifurcation diagrams, and basin of attraction. Complex phenomena, especially coexisting dynamics, chaos and transient chaos emerge in the neural network. Finally, the circuit simulation results verify the effectiveness of theoretical analyses and mathematical simulation and further illustrate the feasibility of the two-neuron based memristive Hopfield neural network hardware.
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Wang, Jianwei, Jialu He, Wei Chen, and Bo Xu. "Abnormal dynamics of cascading edge failures with congestion effect." International Journal of Modern Physics C 29, no. 10 (October 2018): 1850095. http://dx.doi.org/10.1142/s012918311850095x.
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Considering congestion effects in realistic network environments, we give a new method to adjust dynamically adjust the weight of the congested edge. We calculate the load on an edge based on the revised betweenness method and propose a novel model with three states of edges to investigate the dynamics of cascading failures in the ring network, the BA scale-free network, and the real traffic networks in London. By two robust metrics, we surprisingly observe the abnormal dynamics of cascading propagation, especially compared with that in the unadjustable weight, the curves of cascading dynamics in the adjustable weight are more irregular, which means that enhancing the capacity of each edge is not always better to avoid the cascading propagation. In addition, our simulation results show that the dynamical change of the edge’s weight makes the heterogeneous BA networks more vulnerable.
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Andria, Joseph, Giacomo di Tollo, and Jaan Kalda. "Propagation of Bankruptcy Risk over Scale-Free Economic Networks." Entropy 24, no. 12 (November 24, 2022): 1713. http://dx.doi.org/10.3390/e24121713.
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The propagation of bankruptcy-induced shocks across domestic and global economies is sometimes very dramatic; this phenomenon can be modelled as a dynamical process in economic networks. Economic networks are usually scale-free, and scale-free networks are known to be vulnerable with respect to targeted attacks, i.e., attacks directed towards the biggest nodes of the network. Here we address the following question: to what extent does the scale-free nature of economic networks and the vulnerability of the biggest nodes affect the propagation of economic shocks? We model the dynamics of bankruptcies as the propagation of financial contagion across the banking sector over a scale-free network of banks, and perform Monte-Carlo simulations based on synthetic networks. In addition, we analyze the public data regarding the bankruptcy of US banks from the Federal Deposit Insurance Corporation. The dynamics of the shock propagation is characterized in terms of the Bank Failures Diffusion Index, i.e., the average number of new bankruptcies triggered by the bankruptcy of a single bank, and in terms of the Shannon entropy of the whole network. The simulation results are in-line with the empirical findings, and indicate the important role of the biggest banks in the dynamics of economic shocks.
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House, Thomas, and Matt J. Keeling. "Insights from unifying modern approximations to infections on networks." Journal of The Royal Society Interface 8, no. 54 (June 10, 2010): 67–73. http://dx.doi.org/10.1098/rsif.2010.0179.
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Networks are increasingly central to modern science owing to their ability to conceptualize multiple interacting components of a complex system. As a specific example of this, understanding the implications of contact network structure for the transmission of infectious diseases remains a key issue in epidemiology. Three broad approaches to this problem exist: explicit simulation; derivation of exact results for special networks; and dynamical approximations. This paper focuses on the last of these approaches, and makes two main contributions. Firstly, formal mathematical links are demonstrated between several prima facie unrelated dynamical approximations. And secondly, these links are used to derive two novel dynamical models for network epidemiology, which are compared against explicit stochastic simulation. The success of these new models provides improved understanding about the interaction of network structure and transmission dynamics.
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Xiao, Yunpeng, Bai Wang, Yanbing Liu, Zhixian Yan, Xian Chen, Bin Wu, Guangxia Xu, and Yuanni Liu. "Analyzing, Modeling, and Simulation for Human Dynamics in Social Network." Abstract and Applied Analysis 2012 (2012): 1–16. http://dx.doi.org/10.1155/2012/208791.
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This paper studies the human behavior in the top-one social network system in China (Sina Microblog system). By analyzing real-life data at a large scale, we find that the message releasing interval (intermessage time) obeys power law distribution both at individual level and at group level. Statistical analysis also reveals that human behavior in social network is mainly driven by four basic elements:social pressure,social identity,social participation, andsocial relationbetween individuals. Empirical results present the four elements' impact on the human behavior and the relation between these elements. To further understand the mechanism of such dynamic phenomena, a hybrid human dynamic model which combines “interest” of individual and “interaction” among people is introduced, incorporating the four elements simultaneously. To provide a solid evaluation, we simulate both two-agent and multiagent interactions with real-life social network topology. We achieve the consistent results between empirical studies and the simulations. The model can provide a good understanding of human dynamics in social network.
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HÄNGGI, MARTIN, SIMON MOSER, ERIC PFAFFHAUSER, and GEORGE S. MOSCHYTZ. "SIMULATION AND VISUALIZATION OF CNN DYNAMICS." International Journal of Bifurcation and Chaos 09, no. 07 (July 1999): 1237–61. http://dx.doi.org/10.1142/s0218127499000882.
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A new simulator for Cellular Neural Networks (CNNs) is presented. In contrast to other simulators, the CNN cells are visualized in a grid structure, the values of input and states being represented by colors. Input and initial images can easily be generated and changed even while the integration of the system is in progress, and an oscilloscope function allows the quantitative study of CNN transients, thus providing insight into the dynamics of the network. For those who are new to the world of CNNs, a series of predefined templates set and demonstrations are available, which makes the simulator a valuable educational tool. Advanced users and CNN expert can examine manually-entered and parametrized templates and carry out experiments in a very broad spectrum of CNN theory and applications, including quantitative behavior, robustness aspects, settling time, state limitations, different output functions and numerical integration methods. The simulator is written in Java and publicly available on WWW and will run on any Web browser of the newer generations.
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Liang, Junhao, and Changsong Zhou. "Criticality enhances the multilevel reliability of stimulus responses in cortical neural networks." PLOS Computational Biology 18, no. 1 (January 31, 2022): e1009848. http://dx.doi.org/10.1371/journal.pcbi.1009848.
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Cortical neural networks exhibit high internal variability in spontaneous dynamic activities and they can robustly and reliably respond to external stimuli with multilevel features–from microscopic irregular spiking of neurons to macroscopic oscillatory local field potential. A comprehensive study integrating these multilevel features in spontaneous and stimulus–evoked dynamics with seemingly distinct mechanisms is still lacking. Here, we study the stimulus–response dynamics of biologically plausible excitation–inhibition (E–I) balanced networks. We confirm that networks around critical synchronous transition states can maintain strong internal variability but are sensitive to external stimuli. In this dynamical region, applying a stimulus to the network can reduce the trial-to-trial variability and shift the network oscillatory frequency while preserving the dynamical criticality. These multilevel features widely observed in different experiments cannot simultaneously occur in non-critical dynamical states. Furthermore, the dynamical mechanisms underlying these multilevel features are revealed using a semi-analytical mean-field theory that derives the macroscopic network field equations from the microscopic neuronal networks, enabling the analysis by nonlinear dynamics theory and linear noise approximation. The generic dynamical principle revealed here contributes to a more integrative understanding of neural systems and brain functions and incorporates multimodal and multilevel experimental observations. The E–I balanced neural network in combination with the effective mean-field theory can serve as a mechanistic modeling framework to study the multilevel neural dynamics underlying neural information and cognitive processes.
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Liu, Qiang, Wei Zhu, Feng Ma, Xiyu Jia, Yu Gao, and Jun Wen. "Graph attention network-based fluid simulation model." AIP Advances 12, no. 9 (September 1, 2022): 095114. http://dx.doi.org/10.1063/5.0122165.
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Traditional computational fluid dynamics (CFD) techniques deduce the dynamic variations in flow fields by using finite elements or finite differences to solve partial differential equations. CFD usually involves several tens of thousands of grid nodes, which entail long computation times and significant computational resources. Fluid data are usually irregular data, and there will be turbulence in the flow field where the physical quantities between adjacent grid nodes are extremely nonequilibrium. We use a graph attention neural network to build a fluid simulation model (GAFM). GAFM assigns weights to adjacent node-pairs through a graph attention mechanism. In this way, it is not only possible to directly calculate the fluid data but also to adjust for nonequilibrium in vortices, especially turbulent flows. The GAFM deductively predicts the dynamic variations in flow fields by using spatiotemporally continuous sample data. A validation of the proposed GAFM against the two-dimensional (2D) flow around a cylinder confirms its high prediction accuracy. In addition, the GAFM achieves faster computation speeds than traditional CFD solvers by two to three orders of magnitude. The GAFM provides a new idea for the rapid optimization and design of fluid mechanics models and the real-time control of intelligent fluid mechanisms.
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Yang, Qirui, Xiaoqun Wu, and Ziye Fan. "A model for analyzing competitive dynamics on triplex networks." Chaos: An Interdisciplinary Journal of Nonlinear Science 32, no. 3 (March 2022): 033107. http://dx.doi.org/10.1063/5.0081003.
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This paper studies the evolution process of competitive dynamics on triplex complex networks. We propose a new triplex network model in which the state of the node in each layer is affected by its neighbors as well as inter-layer competition. Through this model, we combine the opinion diffusion model, the Ising model, and the signed network and extend their application from single-layer to multi-layer networks. We derive the evolution process and dynamical equations of the model and carry out a series of numerical simulations to discuss the influence of several factors on the evolution process and the competitiveness of the network. First, we find that the increase of global transition threshold [Formula: see text] or the proportion of initial active nodes will lead to more surviving layers and more active nodes in each layer. In addition, we summarize the similarities and differences of the evolution curves under different conditions. Second, we discuss the influence of initial active nodes and the average degree on the competitiveness of the network and find the correlations between them. Finally, we study the relationship between network topology and network competitiveness and conclude the conditions for the best competitiveness of the network. Based on the simulation results, we give specific suggestions on how to improve the competitiveness of the platform in reality.
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Fodor, Oana, Alina Fleștea, Iulian Onija, and Petru Curșeu. "Networks Originate in Minds: An Exploration of Trust Self-Enhancement and Network Centrality in Multiparty Systems." Administrative Sciences 8, no. 4 (October 9, 2018): 60. http://dx.doi.org/10.3390/admsci8040060.
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Multiparty systems (MPSs) are defined as collaborative task-systems composed of various stakeholders (organizations or their representatives) that deal with complex issues that cannot be addressed by a single group or organization. Our study uses a behavioral simulation in which six stakeholder groups engage in interactions in order to reach a set of agreements with respect to complex educational policies. We use a social network perspective to explore the dynamics of network centrality during intergroup interactions in the simulation and show that trust self-enhancement at the onset of the simulation has a positive impact on the evolution of network centrality throughout the simulation. Our results have important implications for the social networks dynamics in MPSs and point towards the benefit of using social network analytics as exploration and/or facilitating tools in MPSs.
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TADIĆ, BOSILJKA, G. J. RODGERS, and STEFAN THURNER. "TRANSPORT ON COMPLEX NETWORKS: FLOW, JAMMING AND OPTIMIZATION." International Journal of Bifurcation and Chaos 17, no. 07 (July 2007): 2363–85. http://dx.doi.org/10.1142/s0218127407018452.
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Many transport processes on networks depend crucially on the underlying network geometry, although the exact relationship between the structure of the network and the properties of transport processes remain elusive. In this paper, we address this question by using numerical models in which both structure and dynamics are controlled systematically. We consider the traffic of information packets that include driving, searching and queuing. We present the results of extensive simulations on two classes of networks; a correlated cyclic scale-free network and an uncorrelated homogeneous weakly clustered network. By measuring different dynamical variables in the free flow regime we show how the global statistical properties of the transport are related to the temporal fluctuations at individual nodes (the traffic noise) and the links (the traffic flow). We then demonstrate that these two network classes appear as representative topologies for optimal traffic flow in the regimes of low density and high density traffic, respectively. We also determine statistical indicators of the pre-jamming regime on different network geometries and discuss the role of queuing and dynamical betweenness for the traffic congestion. The transition to the jammed traffic regime at a critical posting rate on different network topologies is studied as a phase transition with an appropriate order parameter. We also address several open theoretical problems related to the network dynamics.
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Kaneko, Yoshihisa, S. Hirota, and Satoshi Hashimoto. "Discrete Dislocation Dynamics Simulation on Strengths of Dislocation Network Stacks in Multilayered Structures." Key Engineering Materials 353-358 (September 2007): 1086–89. http://dx.doi.org/10.4028/www.scientific.net/kem.353-358.1086.
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Strengths of multilayered structures have been investigated using three-dimensional discrete dislocation dynamics (DDD) simulation. The multilayered structure was modeled as a stack of misfit dislocation networks which must exist at an interface between adjoining crystals having different lattice constants. Passages of a single mobile dislocation through several kinds of network stacks were simulated. The critical stress required for the dislocation passage depended on the dislocation spacing of the network, the number of network sheet and the spacing between network sheets.
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ALONSO-SANZ, RAMÓN, and LARRY BULL. "ON MINIMALLY COUPLED BOOLEAN NETWORKS." International Journal of Bifurcation and Chaos 19, no. 04 (April 2009): 1401–14. http://dx.doi.org/10.1142/s0218127409023743.
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Traditional Boolean networks consist of nodes within a single network, each updating synchronously, although asynchronous versions have also been presented. In this paper the dynamics of two, mutually coupled traditional networks are investigated. In particular, the effects of varying the degree and type of intra-network connectivity are explored. The effects from different inter-network evolution rates are then considered, i.e. asynchronousity at the network level is examined. Finally, state memory is included within the nodes of coupled networks and shown to alter the dynamics of the networks under certain circumstances.
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Srinivasan, Karthik K., and Zhiyong Guo. "Day-to-Day Evolution of Network Flows Under Departure Time Dynamics in Commuter Decisions." Transportation Research Record: Journal of the Transportation Research Board 1831, no. 1 (January 2003): 47–56. http://dx.doi.org/10.3141/1831-06.
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Day-to-day dynamics in an urban traffic network induced by departure time dynamics in commuter decisions are investigated. This investigation relaxes some key restrictions about fixed departure time and equilibrium assumptions to analyze the stability and performance of urban traffic networks over a multiple day planning horizon. A simulation-based framework is developed to analyze day-to-day dynamics by integrating an empirically calibrated model of dynamic departure time decisions with a dynamic network assignment model. Computational experiments are used to investigate the effect of the following experimental factors: recurrent network congestion level, time-dependent loading profile, and users’ sensitivity to commute experience and trip-time volatility on network performance and reliability. The findings provide evidence of considerable day-to-day variations and stochasticity in network flows and performance, even under the assumption of fixed routes and in the absence of information. The results indicate that ( a) the network performance under departure time dynamics can deviate significantly from equilibrium; ( b) the departure time adjustment process is remarkably stable and reaches stationarity, although the departure time choices do not appear to be at equilibrium; ( c) departure time dynamics introduce significant volatility in trip times from day to day; and ( d) increasing the sensitivity of users to commute and network performance attributes (schedule delay, trip-time variability) can lead to more stable system behavior and reliability. These results have important implications for estimation of time-dependent origin–destination matrices, dynamic network analysis, and effective congestion management strategies.
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Khan, Bilal, Kirk Dombrowski, Mohamed Saad, Katherine McLean, and Samuel Friedman. "Network Firewall Dynamics and the Subsaturation Stabilization of HIV." Discrete Dynamics in Nature and Society 2013 (2013): 1–16. http://dx.doi.org/10.1155/2013/720818.
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In 2001, Friedman et al. conjectured the existence of a “firewall effect” in which individuals who are infected with HIV, but remain in a state of low infectiousness, serve to prevent the virus from spreading. To evaluate this historical conjecture, we develop a new graph-theoretic measure that quantifies the extent to which Friedman’s firewall hypothesis (FH) holds in a risk network. We compute this new measure across simulated trajectories of a stochastic discrete dynamical system that models a social network of 25,000 individuals engaging in risk acts over a period of 15 years. The model’s parameters are based on analyses of data collected in prior studies of the real-world risk networks of people who inject drugs (PWID) in New York City. Analysis of system trajectories reveals the structural mechanisms by which individuals with mature HIV infections tend to partition the network into homogeneous clusters (with respect to infection status) and how uninfected clusters remain relatively stable (with respect to infection status) over long stretches of time. We confirm the spontaneous emergence of network firewalls in the system and reveal their structural role in the nonspreading of HIV.
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Tikkala, Anneli, and Martti Juhola. "A neural network simulation of aphasic naming errors: Network dynamics and control." Neurocomputing 13, no. 1 (September 1996): 11–29. http://dx.doi.org/10.1016/0925-2312(95)00082-8.
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Peng, Hao, Wangxin Peng, Dandan Zhao, Zhaolong Hu, Jianmin Han, and Zhonglong Zheng. "Impact of Immunization Strategies on the Dynamics of Social Contagions." Discrete Dynamics in Nature and Society 2020 (August 1, 2020): 1–9. http://dx.doi.org/10.1155/2020/5284348.
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Immunization strategies on complex networks are effective methods to control the spreading dynamics on complex networks, which change the topology and connectivity of the underlying network, thereby affecting the dynamics process of propagation. Here, we use a non-Markovian threshold model to study the impact of immunization strategies on social contagions, in which the immune index greater than (or equal to) 0 corresponds to targeted (random) immunization, and when the immune index is less than 0, the probability of an individual being immunized is inversely related to the degree of the individual. A generalized edge-based compartmental theory is developed to analyze the dynamics of social contagions under immunization, and theoretical predictions are very consistent with simulation results. We find that increasing the immune index or increasing the immune ratio will reduce the final adoption size and increase the outbreak threshold, in other words, make the residual network after immunization not conducive to social contagions. Interestingly, enhancing the network heterogeneity is proved to help improve the immune efficiency of targeted immunization. Besides, the dependence of the outbreak threshold on the network heterogeneity is correlated with the immune ratio and immune index.
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Burakov, M. V., V. F. Shishlakov, and A. S. Konovalov. "ADAPTIVE NEURAL NETWORK PID CONTROLLER." Issues of radio electronics, no. 10 (October 20, 2018): 86–92. http://dx.doi.org/10.21778/2218-5453-2018-10-86-92.
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The problem of constructing an adaptive PID controller based on the Hopfield neural network for a linear dynamic plant of the second order is considered. A description of the plant in the form of a discrete transfer function is used, the coefficients of which are determined with the help of a neural network that minimizes the discrepancy between the outputs of the plant and the model. The neural network processes the current and delayed input and output signals of the plant, forming an output for estimating the coefficients of the model. Another neural network determines the PID regulator coefficients at which the dynamics of the system approach the dynamics of the reference process. The calculation of weights and displacements of neurons in Hopfield networks used for identification and control is based on the construction of Lyapunov functions. The proposed methodology can be used to organize adaptive control of a wide class of linear dynamic systems with variable parameters. The results of the simulation in the article show the effectiveness of the proposed method.
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El Hamidi, Khadija, Mostafa Mjahed, Abdeljalil El Kari, and Hassan Ayad. "Adaptive Control Using Neural Networks and Approximate Models for Nonlinear Dynamic Systems." Modelling and Simulation in Engineering 2020 (August 26, 2020): 1–13. http://dx.doi.org/10.1155/2020/8642915.
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In this research, a comparative study of two recurrent neural networks, nonlinear autoregressive with exogenous input (NARX) neural network and nonlinear autoregressive moving average (NARMA-L2), and a feedforward neural network (FFNN) is performed for their ability to provide adaptive control of nonlinear systems. Three dynamical nonlinear systems of different complexity are considered. The aim of this work is to make the output of the plant follow the desired reference trajectory. The problem becomes more challenging when the dynamics of the plants are assumed to be unknown, and to tackle this problem, a multilayer neural network-based approximate model is set up which will work in parallel to the plant and the control scheme. The network parameters are updated using the dynamic backpropagation (BP) algorithm.
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Lotito, Quintino Francesco, Davide Zanella, and Paolo Casari. "Realistic Aspects of Simulation Models for Fake News Epidemics over Social Networks." Future Internet 13, no. 3 (March 17, 2021): 76. http://dx.doi.org/10.3390/fi13030076.
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The pervasiveness of online social networks has reshaped the way people access information. Online social networks make it common for users to inform themselves online and share news among their peers, but also favor the spreading of both reliable and fake news alike. Because fake news may have a profound impact on the society at large, realistically simulating their spreading process helps evaluate the most effective countermeasures to adopt. It is customary to model the spreading of fake news via the same epidemic models used for common diseases; however, these models often miss concepts and dynamics that are peculiar to fake news spreading. In this paper, we fill this gap by enriching typical epidemic models for fake news spreading with network topologies and dynamics that are typical of realistic social networks. Specifically, we introduce agents with the role of influencers and bots in the model and consider the effects of dynamical network access patterns, time-varying engagement, and different degrees of trust in the sources of circulating information. These factors concur with making the simulations more realistic. Among other results, we show that influencers that share fake news help the spreading process reach nodes that would otherwise remain unaffected. Moreover, we emphasize that bots dramatically speed up the spreading process and that time-varying engagement and network access change the effectiveness of fake news spreading.