Journal articles: 'Dynamics network'

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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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Sun, Zejun, Jinfang Sheng, Bin Wang, Aman Ullah, and FaizaRiaz Khawaja. "Identifying Communities in Dynamic Networks Using Information Dynamics." Entropy 22, no. 4 (April 9, 2020): 425. http://dx.doi.org/10.3390/e22040425.

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Identifying communities in dynamic networks is essential for exploring the latent network structures, understanding network functions, predicting network evolution, and discovering abnormal network events. Many dynamic community detection methods have been proposed from different viewpoints. However, identifying the community structure in dynamic networks is very challenging due to the difficulty of parameter tuning, high time complexity and detection accuracy decreasing as time slices increase. In this paper, we present a dynamic community detection framework based on information dynamics and develop a dynamic community detection algorithm called DCDID (dynamic community detection based on information dynamics), which uses a batch processing technique to incrementally uncover communities in dynamic networks. DCDID employs the information dynamics model to simulate the exchange of information among nodes and aims to improve the efficiency of community detection by filtering out the unchanged subgraph. To illustrate the effectiveness of DCDID, we extensively test it on synthetic and real-world dynamic networks, and the results demonstrate that the DCDID algorithm is superior to the representative methods in relation to the quality of dynamic community detection.

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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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Hu, Ziping, Krishnaiyan Thulasiraman, and Pramode K. Verma. "Complex Networks: Traffic Dynamics, Network Performance, and Network Structure." American Journal of Operations Research 03, no. 01 (2013): 187–95. http://dx.doi.org/10.4236/ajor.2013.31a018.

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Hütt, Marc-Thorsten, Marcus Kaiser, and Claus C. Hilgetag. "Perspective: network-guided pattern formation of neural dynamics." Philosophical Transactions of the Royal Society B: Biological Sciences 369, no. 1653 (October 5, 2014): 20130522. http://dx.doi.org/10.1098/rstb.2013.0522.

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The understanding of neural activity patterns is fundamentally linked to an understanding of how the brain's network architecture shapes dynamical processes. Established approaches rely mostly on deviations of a given network from certain classes of random graphs. Hypotheses about the supposed role of prominent topological features (for instance, the roles of modularity, network motifs or hierarchical network organization) are derived from these deviations. An alternative strategy could be to study deviations of network architectures from regular graphs (rings and lattices) and consider the implications of such deviations for self-organized dynamic patterns on the network. Following this strategy, we draw on the theory of spatio-temporal pattern formation and propose a novel perspective for analysing dynamics on networks, by evaluating how the self-organized dynamics are confined by network architecture to a small set of permissible collective states. In particular, we discuss the role of prominent topological features of brain connectivity, such as hubs, modules and hierarchy, in shaping activity patterns. We illustrate the notion of network-guided pattern formation with numerical simulations and outline how it can facilitate the understanding of neural dynamics.

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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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Chen, Kevin S. "Optimal Population Coding for Dynamic Input by Nonequilibrium Networks." Entropy 24, no. 5 (April 25, 2022): 598. http://dx.doi.org/10.3390/e24050598.

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The efficient coding hypothesis states that neural response should maximize its information about the external input. Theoretical studies focus on optimal response in single neuron and population code in networks with weak pairwise interactions. However, more biological settings with asymmetric connectivity and the encoding for dynamical stimuli have not been well-characterized. Here, we study the collective response in a kinetic Ising model that encodes the dynamic input. We apply gradient-based method and mean-field approximation to reconstruct networks given the neural code that encodes dynamic input patterns. We measure network asymmetry, decoding performance, and entropy production from networks that generate optimal population code. We analyze how stimulus correlation, time scale, and reliability of the network affect optimal encoding networks. Specifically, we find network dynamics altered by statistics of the dynamic input, identify stimulus encoding strategies, and show optimal effective temperature in the asymmetric networks. We further discuss how this approach connects to the Bayesian framework and continuous recurrent neural networks. Together, these results bridge concepts of nonequilibrium physics with the analyses of dynamics and coding in networks.

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Chen, Kevin S. "Optimal Population Coding for Dynamic Input by Nonequilibrium Networks." Entropy 24, no. 5 (April 25, 2022): 598. http://dx.doi.org/10.3390/e24050598.

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The efficient coding hypothesis states that neural response should maximize its information about the external input. Theoretical studies focus on optimal response in single neuron and population code in networks with weak pairwise interactions. However, more biological settings with asymmetric connectivity and the encoding for dynamical stimuli have not been well-characterized. Here, we study the collective response in a kinetic Ising model that encodes the dynamic input. We apply gradient-based method and mean-field approximation to reconstruct networks given the neural code that encodes dynamic input patterns. We measure network asymmetry, decoding performance, and entropy production from networks that generate optimal population code. We analyze how stimulus correlation, time scale, and reliability of the network affect optimal encoding networks. Specifically, we find network dynamics altered by statistics of the dynamic input, identify stimulus encoding strategies, and show optimal effective temperature in the asymmetric networks. We further discuss how this approach connects to the Bayesian framework and continuous recurrent neural networks. Together, these results bridge concepts of nonequilibrium physics with the analyses of dynamics and coding in networks.

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10

Chen, Kevin S. "Optimal Population Coding for Dynamic Input by Nonequilibrium Networks." Entropy 24, no. 5 (April 25, 2022): 598. http://dx.doi.org/10.3390/e24050598.

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The efficient coding hypothesis states that neural response should maximize its information about the external input. Theoretical studies focus on optimal response in single neuron and population code in networks with weak pairwise interactions. However, more biological settings with asymmetric connectivity and the encoding for dynamical stimuli have not been well-characterized. Here, we study the collective response in a kinetic Ising model that encodes the dynamic input. We apply gradient-based method and mean-field approximation to reconstruct networks given the neural code that encodes dynamic input patterns. We measure network asymmetry, decoding performance, and entropy production from networks that generate optimal population code. We analyze how stimulus correlation, time scale, and reliability of the network affect optimal encoding networks. Specifically, we find network dynamics altered by statistics of the dynamic input, identify stimulus encoding strategies, and show optimal effective temperature in the asymmetric networks. We further discuss how this approach connects to the Bayesian framework and continuous recurrent neural networks. Together, these results bridge concepts of nonequilibrium physics with the analyses of dynamics and coding in networks.

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LI, AMING, and YANG-YU LIU. "CONTROLLING NETWORK DYNAMICS." Advances in Complex Systems 22, no. 07n08 (November 2019): 1950021. http://dx.doi.org/10.1142/s0219525919500218.

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Network science has experienced unprecedented rapid development in the past two decades. The network perspective has also been widely applied to explore various complex systems in great depth. In the first decade, fundamental characteristics of complex network structure, such as the small-worldness, scale-freeness, and modularity, of various complex networked systems were harvested from analyzing big empirical data. The associated dynamical processes on complex networks were also heavily studied. In the second decade, more attention was devoted to investigating the control of complex networked systems, ranging from fundamental theories to practical applications. Here we briefly review the recent progress regarding network dynamics and control, mainly concentrating on research questions proposed in the six papers we collected for this topical issue. This review closes with possible research directions along this line, and several important problems to be solved. We expect that, in the near future, network control will play an even bigger role in more fields, helping us understand and control many complex natural and engineered systems.

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Beiran, Manuel, Alexis Dubreuil, Adrian Valente, Francesca Mastrogiuseppe, and Srdjan Ostojic. "Shaping Dynamics With Multiple Populations in Low-Rank Recurrent Networks." Neural Computation 33, no. 6 (May 13, 2021): 1572–615. http://dx.doi.org/10.1162/neco_a_01381.

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An emerging paradigm proposes that neural computations can be understood at the level of dynamic systems that govern low-dimensional trajectories of collective neural activity. How the connectivity structure of a network determines the emergent dynamical system, however, remains to be clarified. Here we consider a novel class of models, gaussian-mixture, low-rank recurrent networks in which the rank of the connectivity matrix and the number of statistically defined populations are independent hyperparameters. We show that the resulting collective dynamics form a dynamical system, where the rank sets the dimensionality and the population structure shapes the dynamics. In particular, the collective dynamics can be described in terms of a simplified effective circuit of interacting latent variables. While having a single global population strongly restricts the possible dynamics, we demonstrate that if the number of populations is large enough, a rank R network can approximate any R-dimensional dynamical system.

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Nie, Chun-Xiao. "Hurst analysis of dynamic networks." Chaos: An Interdisciplinary Journal of Nonlinear Science 32, no. 2 (February 2022): 023130. http://dx.doi.org/10.1063/5.0070170.

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The sequence of network snapshots with time stamps is an effective tool for describing system dynamics. First, this article constructs a multifractal analysis of a snapshot network, in which the Hurst integral is used to describe the fractal structure hidden in structural dynamics. Second, we adjusted the network model and conducted comparative analysis to clarify the meaning of the Hurst exponent and found that the snapshot network usually includes multiple fractal structures, such as local and global fractal structures. Finally, we discussed the fractal structure of two real network datasets. We found that the real snapshot network also includes rich dynamics, which can be distinguished by the Hurst exponent. In particular, the dynamics of financial networks includes multifractal structures. This article provides a perspective to study the dynamic networks, thereby indirectly describing the fractal characteristics of complex system dynamics.

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Savla, Ketan, Jeff S. Shamma, and Munther A. Dahleh. "Network Effects on the Robustness of Dynamic Systems." Annual Review of Control, Robotics, and Autonomous Systems 3, no. 1 (May 3, 2020): 115–49. http://dx.doi.org/10.1146/annurev-control-091219-012549.

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We review selected results related to the robustness of networked systems in finite and asymptotically large size regimes in static and dynamical settings. In the static setting, within the framework of flow over finite networks, we discuss the effect of physical constraints on robustness to loss in link capacities. In the dynamical setting, we review several settings in which small-gain-type analysis provides tight robustness guarantees for linear dynamics over finite networks toward worst-case and stochastic disturbances. We discuss network flow dynamic settings where nonlinear techniques facilitate understanding the effect, on robustness, of constraints on capacity and information, substituting information with control action, and cascading failure. We also contrast cascading failure with a representative contagion model. For asymptotically large networks, we discuss the role of network properties in connecting microscopic shocks to emergent macroscopic fluctuations under linear dynamics as well as for economic networks at equilibrium. Through this review, we aim to achieve two objectives: to highlight selected settings in which the role of the interconnectivity structure of a network in its robustness is well understood, and to highlight a few additional settings in which existing system-theoretic tools give tight robustness guarantees and that are also appropriate avenues for future network-theoretic investigations.

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Lyons, Rowanne, Larissa Hammer, Alexis André, Charles-André Fustin, Renaud Nicolaÿ, and Evelyne van Ruymbeke. "Equilibration dynamics of a dynamic covalent network diluted in a metallosupramolecular polymer matrix." Journal of Rheology 66, no. 6 (November 1, 2022): 1349–64. http://dx.doi.org/10.1122/8.0000473.

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We investigate the viscoelastic properties of double dynamic networks (DDNs) based on side-functionalized P nBA chains. One of these networks is highly crosslinked by metal-ligand junctions characterized by a fast association/dissociation dynamics, while the other network is sparsely crosslinked with slow dynamic covalent networks (DCNs). We first show that modulating the dynamics of the metallosupramolecular networks, by playing with the temperature, the density of reversible junctions, or the stress applied, has direct consequences on the local equilibration of the DCN. The latter takes place by a constraint release Rouse process at the rhythm of the association/dissociation of the metal-ligand junctions. Then, based on creep-recovery experiments, we investigate the ability of the DDNs to recover their initial shape after a creep test and show again the important role played by the metallosupramolecular network. In particular, the sample recovery strongly depends on the network connectivity, which is enhanced if a denser metallosupramolecular network is used as it reduces the possible creep of the double dynamic network and increases its elastic memory. The sample recovery also depends on the association-dissociation dynamics of the metallosupramolecular bonds as it fixes how fast the stretched DCN can come back to its equilibrium conformation and can recover its initial shape after a large deformation has been applied. Adjusting the dynamics of the weak network is thus a key process to govern the viscoelastic response of the slow network.

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Mei, Zhuanglin, and Toshiki Oguchi. "Network Structure Identification Based on Measured Output Data Using Koopman Operators." Mathematics 11, no. 1 (December 26, 2022): 89. http://dx.doi.org/10.3390/math11010089.

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This paper considers the identification problem of network structures of interconnected dynamical systems using measured output data. In particular, we propose an identification method based on the measured output data of each node in the network whose dynamic is unknown. The proposed identification method consists of three steps: we first consider the outputs of the nodes to be all the states of the dynamics of the nodes, and the unmeasurable hidden states to be dynamical inputs with unknown dynamics. In the second step, we define the dynamical inputs as new variables and identify the dynamics of the network system with measured output data using Koopman operators. Finally, we extract the network structure from the identified dynamics as the information transmitted via the network. We show that the identified coupling functions, which represent the network structures, are actually projections of the dynamical inputs onto the space spanned by some observable functions. Numerical examples illustrate the validity of the obtained results.

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Fuster, Joaquin M. "Cellular Dynamics of Network Memory." Zeitschrift für Naturforschung C 53, no. 7-8 (August 1, 1998): 670–76. http://dx.doi.org/10.1515/znc-1998-7-819.

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Abstract One example of “emergence” is the development, as a result of neural ontogeny and living experience, of cortical networks capable of representing and retaining cognitive information. A large body of evidence from neuropsychology, electrophysiology and neuroimaging indicates that so-called working memory and long-term memory share the same neural substrate in the cerebral cortex. That substrate consists in a system of widespread, overlapping and hierarchically organized networks of cortical neurons. In this system, any neuron or group of neurons can be part of many networks, and thus many memories. Working memory is the temporary activation of one such network of long-term memory for the purpose of executing an action in the near future. The activation of the network may be brought about by stimuli that by virtue of prior experience are in some manner associated with the cognitive content of the network, including the response of the organism to those stimuli. The mechanisms by which the network stays activated are presumed to include the recurrent re-entry of impulses through associated neuronal assemblies of the network. Consistent with this notion is the following evidence: (1) working memory depends on the functional integrity of cortico-corti-cal connective loops; and (2) during working memory, remarkable similarities -including “attractor behavior” -have been observed between firing patterns in real cortex and in an artificial recurrent network.

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Jackson, Nicholas E., Lin X. Chen, and Mark A. Ratner. "Charge transport network dynamics in molecular aggregates." Proceedings of the National Academy of Sciences 113, no. 31 (July 20, 2016): 8595–600. http://dx.doi.org/10.1073/pnas.1601915113.

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Due to the nonperiodic nature of charge transport in disordered systems, generating insight into static charge transport networks, as well as analyzing the network dynamics, can be challenging. Here, we apply time-dependent network analysis to scrutinize the charge transport networks of two representative molecular semiconductors: a rigid n-type molecule, perylenediimide, and a flexible p-type molecule, bBDT(TDPP)2. Simulations reveal the relevant timescale for local transfer integral decorrelation to be ∼100 fs, which is shown to be faster than that of a crystalline morphology of the same molecule. Using a simple graph metric, global network changes are observed over timescales competitive with charge carrier lifetimes. These insights demonstrate that static charge transport networks are qualitatively inadequate, whereas average networks often overestimate network connectivity. Finally, a simple methodology for tracking dynamic charge transport properties is proposed.

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Vogels, Tim P., Kanaka Rajan, and L. F. Abbott. "NEURAL NETWORK DYNAMICS." Annual Review of Neuroscience 28, no. 1 (July 21, 2005): 357–76. http://dx.doi.org/10.1146/annurev.neuro.28.061604.135637.

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Sugihara, George, and Hao Ye. "Cooperative network dynamics." Nature 458, no. 7241 (April 2009): 979–80. http://dx.doi.org/10.1038/458979a.

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Veenstra, René, Jan Kornelis Dijkstra, Christian Steglich, and Maarten H. W. Van Zalk. "Network-Behavior Dynamics." Journal of Research on Adolescence 23, no. 3 (August 19, 2013): 399–412. http://dx.doi.org/10.1111/jora.12070.

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Chou, Hsin-Hui, and Judy Zolkiewski. "Decoding network dynamics." Industrial Marketing Management 41, no. 2 (February 2012): 247–58. http://dx.doi.org/10.1016/j.indmarman.2012.01.003.

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Herendeen, Robert A. "Network trophic dynamics." Ecological Modelling 42, no. 1 (July 1988): 75–78. http://dx.doi.org/10.1016/0304-3800(88)90093-2.

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Vilar, José M. G., Călin C. Guet, and Stanislas Leibler. "Modeling network dynamics." Journal of Cell Biology 161, no. 3 (May 12, 2003): 471–76. http://dx.doi.org/10.1083/jcb.200301125.

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We use the lac operon in Escherichia coli as a prototype system to illustrate the current state, applicability, and limitations of modeling the dynamics of cellular networks. We integrate three different levels of description (molecular, cellular, and that of cell population) into a single model, which seems to capture many experimental aspects of the system.

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Dragicevic, Arnaud Z., and Bernard Sinclair-Desgagné. "Sustainable network dynamics." Ecological Modelling 270 (December 2013): 43–53. http://dx.doi.org/10.1016/j.ecolmodel.2013.09.003.

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Melamed, David, Ashley Harrell, and Brent Simpson. "Cooperation, clustering, and assortative mixing in dynamic networks." Proceedings of the National Academy of Sciences 115, no. 5 (January 16, 2018): 951–56. http://dx.doi.org/10.1073/pnas.1715357115.

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Humans’ propensity to cooperate is driven by our embeddedness in social networks. A key mechanism through which networks promote cooperation is clustering. Within clusters, conditional cooperators are insulated from exploitation by noncooperators, allowing them to reap the benefits of cooperation. Dynamic networks, where ties can be shed and new ties formed, allow for the endogenous emergence of clusters of cooperators. Although past work suggests that either reputation processes or network dynamics can increase clustering and cooperation, existing work on network dynamics conflates reputations and dynamics. Here we report results from a large-scale experiment (total n = 2,675) that embedded participants in clustered or random networks that were static or dynamic, with varying levels of reputational information. Results show that initial network clustering predicts cooperation in static networks, but not in dynamic ones. Further, our experiment shows that while reputations are important for partner choice, cooperation levels are driven purely by dynamics. Supplemental conditions confirmed this lack of a reputation effect. Importantly, we find that when participants make individual choices to cooperate or defect with each partner, as opposed to a single decision that applies to all partners (as is standard in the literature on cooperation in networks), cooperation rates in static networks are as high as cooperation rates in dynamic networks. This finding highlights the importance of structured relations for sustained cooperation, and shows how giving experimental participants more realistic choices has important consequences for whether dynamic networks promote higher levels of cooperation than static networks.

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Nagatani, Takashi, and Genki Ichinose. "Diffusively-Coupled Rock-Paper-Scissors Game with Mutation in Scale-Free Hierarchical Networks." Complexity 2020 (October 9, 2020): 1–8. http://dx.doi.org/10.1155/2020/6976328.

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We present a metapopulation dynamic model for the diffusively-coupled rock-paper-scissors (RPS) game with mutation in scale-free hierarchical networks. We investigate how the RPS game changes by mutation in scale-free networks. Only the mutation from rock to scissors (R-to-S) occurs with rate μ. In the network, a node represents a patch where the RPS game is performed. RPS individuals migrate among nodes by diffusion. The dynamics are represented by the reaction-diffusion equations with the recursion formula. We study where and how species coexist or go extinct in the scale-free network. We numerically obtained the solutions for the metapopulation dynamics and derived the transition points. The results show that, with increasing mutation rate μ, the extinction of P species occurs and then the extinction of R species occurs, and finally only S species survives. Thus, the first and second dynamical phase transitions occur in the scale-free hierarchical network. We also show that the scaling law holds for the population dynamics which suggests that the transition points approach zero in the limit of infinite size.

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Wu, Xing, Shuai Mao, Luolin Xiong, and Yang Tang. "A survey on temporal network dynamics with incomplete data." Electronic Research Archive 30, no. 10 (2022): 3786–810. http://dx.doi.org/10.3934/era.2022193.

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<abstract><p>With the development of complex network theory, many phenomena on complex networks, such as infectious disease transmission, information spreading and transportation management, can be explained by temporal network dynamics, to reveal the evolution of the real world. Due to the failure of equipment for collecting data, human subjectivity, and false decisions made by machines when the high accuracy is required, data from temporal networks is usually incomplete, which makes the samples unrepresentative and the model analysis more challenging. This survey concentrates on the pre-processing strategies of incomplete data and overviews two categories of methods on data imputation and prediction, respectively. According to whether each layer in temporal networks has the coupling process, this survey overviews the dynamic modeling approaches in terms of both a single process and coupling processes on complex temporal networks. Moreover, for complex temporal networks with incomplete data, this survey summarizes various characteristic analysis methods, which concentrate on critical nodes identification, network reconstruction, network recoverity, and criticality. Finally, some future directions are discussed for temporal networks dynamics with incomplete data.</p></abstract>

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Cao, Shun, and Hiroki Sayama. "Detecting Dynamic States of Temporal Networks Using Connection Series Tensors." Complexity 2020 (December 21, 2020): 1–15. http://dx.doi.org/10.1155/2020/9649310.

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Many temporal networks exhibit multiple system states, such as weekday and weekend patterns in social contact networks. The detection of such distinct states in temporal network data has recently been studied as it helps reveal underlying dynamical processes. A commonly used method is network aggregation over a time window, which aggregates a subsequence of multiple network snapshots into one static network. This method, however, necessarily discards temporal dynamics within the time window. Here we propose a new method for detecting dynamic states in temporal networks using connection series (i.e., time series of connection status) between nodes. Our method consists of the construction of connection series tensors over nonoverlapping time windows, similarity measurement between these tensors, and community detection in the similarity network of those time windows. Experiments with empirical temporal network data demonstrated that our method outperformed the conventional approach using simple network aggregation in revealing interpretable system states. In addition, our method allows users to analyze hierarchical temporal structures and to uncover dynamic states at different spatial/temporal resolutions.

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Sevetson, Jessica L., Brian Theyel, and Diane Hoffman-Kim. "Cortical spheroids display oscillatory network dynamics." Lab on a Chip 21, no. 23 (2021): 4586–95. http://dx.doi.org/10.1039/d1lc00737h.

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3-D spheroid cultures contain networks that develop by 9 days and develop increasingly complex network activity patterns as they mature. We demonstrate, for the first time, that spheroids exhibit network activity similar to in vivo network events.

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Iedema, Rick, Raj Verma, Sonia Wutzke, Nigel Lyons, and Brian McCaughan. "A network of networks." Journal of Health Organization and Management 31, no. 2 (April 10, 2017): 223–36. http://dx.doi.org/10.1108/jhom-07-2016-0146.

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Purpose To further our insight into the role of networks in health system reform, the purpose of this paper is to investigate how one agency, the NSW Agency for Clinical Innovation (ACI), and the multiple networks and enabling resources that it encompasses, govern, manage and extend the potential of networks for healthcare practice improvement. Design/methodology/approach This is a case study investigation which took place over ten months through the first author’s participation in network activities and discussions with the agency’s staff about their main objectives, challenges and achievements, and with selected services around the state of New South Wales to understand the agency’s implementation and large system transformation activities. Findings The paper demonstrates that ACI accommodates multiple networks whose oversight structures, self-organisation and systems change approaches combined in dynamic ways, effectively yield a diversity of network governances. Further, ACI bears out a paradox of “centralised decentralisation”, co-locating agents of innovation with networks of implementation and evaluation expertise. This arrangement strengthens and legitimates the role of the strategic hybrid – the healthcare professional in pursuit of change and improvement, and enhances their influence and impact on the wider system. Research limitations/implications While focussing the case study on one agency only, this study is unique as it highlights inter-network connections. Contributing to the literature on network governance, this paper identifies ACI as a “network of networks” through which resources, expectations and stakeholder dynamics are dynamically and flexibly mediated and enhanced. Practical implications The co-location of and dynamic interaction among clinical networks may create synergies among networks, nurture “strategic hybrids”, and enhance the impact of network activities on health system reform. Social implications Network governance requires more from network members than participation in a single network, as it involves health service professionals and consumers in a multi-network dynamic. This dynamic requires deliberations and collaborations to be flexible, and it increasingly positions members as “strategic hybrids” – people who have moved on from singular taken-as-given stances and identities, towards hybrid positionings and flexible perspectives. Originality/value This paper is novel in that it identifies a critical feature of health service reform and large system transformation: network governance is empowered through the dynamic co-location of and collaboration among healthcare networks, particularly when complemented with “enabler” teams of people specialising in programme implementation and evaluation.

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O'Dea, Reuben, Jonathan J. Crofts, and Marcus Kaiser. "Spreading dynamics on spatially constrained complex brain networks." Journal of The Royal Society Interface 10, no. 81 (April 6, 2013): 20130016. http://dx.doi.org/10.1098/rsif.2013.0016.

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The study of dynamical systems defined on complex networks provides a natural framework with which to investigate myriad features of neural dynamics and has been widely undertaken. Typically, however, networks employed in theoretical studies bear little relation to the spatial embedding or connectivity of the neural networks that they attempt to replicate. Here, we employ detailed neuroimaging data to define a network whose spatial embedding represents accurately the folded structure of the cortical surface of a rat brain and investigate the propagation of activity over this network under simple spreading and connectivity rules. By comparison with standard network models with the same coarse statistics, we show that the cortical geometry influences profoundly the speed of propagation of activation through the network. Our conclusions are of high relevance to the theoretical modelling of epileptic seizure events and indicate that such studies which omit physiological network structure risk simplifying the dynamics in a potentially significant way.

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Deng, Hanming, Yang Hua, Tao Song, Zhengui Xue, Ruhui Ma, Neil Robertson, and Haibing Guan. "Reinforcing Neural Network Stability with Attractor Dynamics." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 3765–72. http://dx.doi.org/10.1609/aaai.v34i04.5787.

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Recent approaches interpret deep neural works (DNNs) as dynamical systems, drawing the connection between stability in forward propagation and generalization of DNNs. In this paper, we take a step further to be the first to reinforce this stability of DNNs without changing their original structure and verify the impact of the reinforced stability on the network representation from various aspects. More specifically, we reinforce stability by modeling attractor dynamics of a DNN and propose relu-max attractor network (RMAN), a light-weight module readily to be deployed on state-of-the-art ResNet-like networks. RMAN is only needed during training so as to modify a ResNet's attractor dynamics by minimizing an energy function together with the loss of the original learning task. Through intensive experiments, we show that RMAN-modified attractor dynamics bring a more structured representation space to ResNet and its variants, and more importantly improve the generalization ability of ResNet-like networks in supervised tasks due to reinforced stability.

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Liao, Wei, Yun-Shuang Fan, Siqi Yang, Jiao Li, Xujun Duan, Qian Cui, and Huafu Chen. "Preservation Effect: Cigarette Smoking Acts on the Dynamic of Influences Among Unifying Neuropsychiatric Triple Networks in Schizophrenia." Schizophrenia Bulletin 45, no. 6 (December 17, 2018): 1242–50. http://dx.doi.org/10.1093/schbul/sby184.

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Abstract Objective The high prevalence of cigarette smoking in schizophrenia (SZ) is generally explained by the self-medication theory. However, its neurobiological mechanism remains unclear. The impaired dynamic of influences among unifying neuropsychiatric triple networks in SZ, including the central executive network (CEN), the default mode network (DMN), and the salience network (SN), might explain the nature of their syndromes, whereas smoking could regulate the dynamics within networks. Therefore, this study examined whether cigarette smoking could elicit a distinct improvement in the dynamics of triple networks in SZ and associated with the alleviation of symptoms. Methods Four groups were recruited, namely, SZ smoking (n = 22)/nonsmoking (n = 25), and healthy controls smoking (n = 22)/nonsmoking (n = 21). All participants underwent a resting-state functional magnetic resonance imaging (fMRI). The dynamics among unifying neuropsychiatric triple networks were measured using Granger causality analysis on the resting-sate fMRI signal. Interaction effects between SZ and smoking on dynamics were detected using 2-way analysis of covariance, correcting for sex, age, and education level. Results Whereas smoking reduced SN→DMN dynamic in healthy controls, it preserved the dynamic in SZ, thus suggesting a preservation effect. Moreover, smoking additionally increased DMN→CEN dynamic in SZ. Conclusions This finding from neural pathways shed new insights into the prevailing self-medication hypothesis in SZ. More broadly, this study elaborates on the neurobiological dynamics that may assist in the treatment of the symptomatology of SZ.

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Sheikhattar, Alireza, Sina Miran, Ji Liu, Jonathan B. Fritz, Shihab A. Shamma, Patrick O. Kanold, and Behtash Babadi. "Extracting neuronal functional network dynamics via adaptive Granger causality analysis." Proceedings of the National Academy of Sciences 115, no. 17 (April 9, 2018): E3869—E3878. http://dx.doi.org/10.1073/pnas.1718154115.

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Quantifying the functional relations between the nodes in a network based on local observations is a key challenge in studying complex systems. Most existing time series analysis techniques for this purpose provide static estimates of the network properties, pertain to stationary Gaussian data, or do not take into account the ubiquitous sparsity in the underlying functional networks. When applied to spike recordings from neuronal ensembles undergoing rapid task-dependent dynamics, they thus hinder a precise statistical characterization of the dynamic neuronal functional networks underlying adaptive behavior. We develop a dynamic estimation and inference paradigm for extracting functional neuronal network dynamics in the sense of Granger, by integrating techniques from adaptive filtering, compressed sensing, point process theory, and high-dimensional statistics. We demonstrate the utility of our proposed paradigm through theoretical analysis, algorithm development, and application to synthetic and real data. Application of our techniques to two-photon Ca2+ imaging experiments from the mouse auditory cortex reveals unique features of the functional neuronal network structures underlying spontaneous activity at unprecedented spatiotemporal resolution. Our analysis of simultaneous recordings from the ferret auditory and prefrontal cortical areas suggests evidence for the role of rapid top-down and bottom-up functional dynamics across these areas involved in robust attentive behavior.

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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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Varona, Pablo, and Mikhail I. Rabinovich. "Hierarchical dynamics of informational patterns and decision-making." Proceedings of the Royal Society B: Biological Sciences 283, no. 1832 (June 15, 2016): 20160475. http://dx.doi.org/10.1098/rspb.2016.0475.

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Traditional studies on the interaction of cognitive functions in healthy and disordered brains have used the analyses of the connectivity of several specialized brain networks—the functional connectome. However, emerging evidence suggests that both brain networks and functional spontaneous brain-wide network communication are intrinsically dynamic. In the light of studies investigating the cooperation between different cognitive functions, we consider here the dynamics of hierarchical networks in cognitive space. We show, using an example of behavioural decision-making based on sequential episodic memory, how the description of metastable pattern dynamics underlying basic cognitive processes helps to understand and predict complex processes like sequential episodic memory recall and competition among decision strategies. The mathematical images of the discussed phenomena in the phase space of the corresponding cognitive model are hierarchical heteroclinic networks. One of the most important features of such networks is the robustness of their dynamics. Different kinds of instabilities of these dynamics can be related to ‘dynamical signatures’ of creativity and different psychiatric disorders. The suggested approach can also be useful for the understanding of the dynamical processes that are the basis of consciousness.

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Ranft, Jonas, and Benjamin Lindner. "A self-consistent analytical theory for rotator networks under stochastic forcing: Effects of intrinsic noise and common input." Chaos: An Interdisciplinary Journal of Nonlinear Science 32, no. 6 (June 2022): 063131. http://dx.doi.org/10.1063/5.0096000.

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Despite the incredible complexity of our brains’ neural networks, theoretical descriptions of neural dynamics have led to profound insights into possible network states and dynamics. It remains challenging to develop theories that apply to spiking networks and thus allow one to characterize the dynamic properties of biologically more realistic networks. Here, we build on recent work by van Meegen and Lindner who have shown that “rotator networks,” while considerably simpler than real spiking networks and, therefore, more amenable to mathematical analysis, still allow one to capture dynamical properties of networks of spiking neurons. This framework can be easily extended to the case where individual units receive uncorrelated stochastic input, which can be interpreted as intrinsic noise. However, the assumptions of the theory do not apply anymore when the input received by the single rotators is strongly correlated among units. As we show, in this case, the network fluctuations become significantly non-Gaussian, which calls for reworking of the theory. Using a cumulant expansion, we develop a self-consistent analytical theory that accounts for the observed non-Gaussian statistics. Our theory provides a starting point for further studies of more general network setups and information transmission properties of these networks.

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ENCISO, GERMAN, RADEK ERBAN, and JINSU KIM. "Identifiability of stochastically modelled reaction networks." European Journal of Applied Mathematics 32, no. 5 (February 15, 2021): 865–87. http://dx.doi.org/10.1017/s0956792520000492.

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Chemical reaction networks describe interactions between biochemical species. Once an underlying reaction network is given for a biochemical system, the system dynamics can be modelled with various mathematical frameworks such as continuous-time Markov processes. In this manuscript, the identifiability of the underlying network structure with a given stochastic system dynamics is studied. It is shown that some data types related to the associated stochastic dynamics can uniquely identify the underlying network structure as well as the system parameters. The accuracy of the presented network inference is investigated when given dynamical data are obtained via stochastic simulations.

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Sivakumar, B., and F. M. Woldemeskel. "Complex networks for streamflow dynamics." Hydrology and Earth System Sciences 18, no. 11 (November 20, 2014): 4565–78. http://dx.doi.org/10.5194/hess-18-4565-2014.

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Abstract. Streamflow modeling is an enormously challenging problem, due to the complex and nonlinear interactions between climate inputs and landscape characteristics over a wide range of spatial and temporal scales. A basic idea in streamflow studies is to establish connections that generally exist, but attempts to identify such connections are largely dictated by the problem at hand and the system components in place. While numerous approaches have been proposed in the literature, our understanding of these connections remains far from adequate. The present study introduces the theory of networks, in particular complex networks, to examine the connections in streamflow dynamics, with a particular focus on spatial connections. Monthly streamflow data observed over a period of 52 years from a large network of 639 monitoring stations in the contiguous US are studied. The connections in this streamflow network are examined primarily using the concept of clustering coefficient, which is a measure of local density and quantifies the network's tendency to cluster. The clustering coefficient analysis is performed with several different threshold levels, which are based on correlations in streamflow data between the stations. The clustering coefficient values of the 639 stations are used to obtain important information about the connections in the network and their extent, similarity, and differences between stations/regions, and the influence of thresholds. The relationship of the clustering coefficient with the number of links/actual links in the network and the number of neighbors is also addressed. The results clearly indicate the usefulness of the network-based approach for examining connections in streamflow, with important implications for interpolation and extrapolation, classification of catchments, and predictions in ungaged basins.

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Sivakumar, B., and F. M. Woldemeskel. "Complex networks for streamflow dynamics." Hydrology and Earth System Sciences Discussions 11, no. 7 (July 2, 2014): 7255–89. http://dx.doi.org/10.5194/hessd-11-7255-2014.

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Abstract. Streamflow modeling is an enormously challenging problem, due to the complex and nonlinear interactions between climate inputs and landscape characteristics over a wide range of spatial and temporal scales. A basic idea in streamflow studies is to establish connections that generally exist, but attempts to identify such connections are largely dictated by the problem at hand and the system components in place. While numerous approaches have been proposed in the literature, our understanding of these connections remains far from adequate. The present study introduces the theory of networks, and in particular complex networks, to examine the connections in streamflow dynamics, with a particular focus on spatial connections. Monthly streamflow data observed over a period of 52 years from a large network of 639 monitoring stations in the contiguous United States are studied. The connections in this streamflow network are examined using the concept of clustering coefficient, which is a measure of local density and quantifies the network's tendency to cluster. The clustering coefficient analysis is performed with several different threshold levels, which are based on correlations in streamflow data between the stations. The clustering coefficient values of the 639 stations are used to obtain important information about the connections in the network and their extent, similarity and differences between stations/regions, and the influence of thresholds. The relationship of the clustering coefficient with the number of links/actual links in the network and the number of neighbors is also addressed. The results clearly indicate the usefulness of the network-based approach for examining connections in streamflow, with important implications for interpolation and extrapolation, classification of catchments, and predictions in ungaged basins.

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Shepherd, Patrick, and Judy Goldsmith. "A Reinforcement Learning Approach to Strategic Belief Revelation with Social Influence." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 10 (April 3, 2020): 13734–35. http://dx.doi.org/10.1609/aaai.v34i10.7139.

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The study of social networks has increased rapidly in the past few decades. Of recent interest are the dynamics of changing opinions over a network. Some research has investigated how interpersonal influence can affect opinion change, how to maximize/minimize the spread of opinion change over a network, and recently, if/how agents can act strategically to effect some outcome in the network's opinion distribution. This latter problem can be modeled and addressed as a reinforcement learning problem; we introduce an approach to help network agents find strategies that outperform hand-crafted policies. Our preliminary results show that our approach is promising in networks with dynamic topologies.

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Zhang, Lei, Zhiqian Chen, Chang-Tien Lu, and Liang Zhao. "From “Dynamics on Graphs” to “Dynamics of Graphs”: An Adaptive Echo-State Network Solution (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (June 28, 2022): 13111–12. http://dx.doi.org/10.1609/aaai.v36i11.21692.

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Many real-world networks evolve over time, which results in dynamic graphs such as human mobility networks and brain networks. Usually, the “dynamics on graphs” (e.g., node attribute values evolving) are observable, and may be related to and indicative of the underlying “dynamics of graphs” (e.g., evolving of the graph topology). Traditional RNN-based methods are not adaptive or scalable for learn- ing the unknown mappings between two types of dynamic graph data. This study presents a AD-ESN, and adaptive echo state network that can automatically learn the best neural net- work architecture for certain data while keeping the efficiency advantage of echo state networks. We show that AD-ESN can successfully discover the underlying pre-defined map- ping function and unknown nonlinear map-ping between time series and graphs.

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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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Kleinbaum, Adam M., and Madeline King Kneeland. "Network Evolution: Exploring the Dynamics of Organizational Networks." Academy of Management Proceedings 2018, no. 1 (August 2018): 17069. http://dx.doi.org/10.5465/ambpp.2018.17069symposium.

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Qiang, Fang, and Huang Shuangquan. "Progress in pollination networks: network structure and dynamics." Biodiversity Science 20, no. 3 (January 7, 2013): 300–307. http://dx.doi.org/10.3724/sp.j.1003.2012.08026.

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Ying, Li, Liu Zeng-Rong, and Zhang Jian-Bao. "Dynamics of network motifs in genetic regulatory networks." Chinese Physics 16, no. 9 (September 2007): 2587–94. http://dx.doi.org/10.1088/1009-1963/16/9/015.

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Aguiar, Manuela A. D., and Ana Paula S. Dias. "Heteroclinic network dynamics on joining coupled cell networks." Dynamical Systems 32, no. 1 (July 4, 2016): 4–22. http://dx.doi.org/10.1080/14689367.2016.1197889.

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Viriyasitavat, Wantanee, Fan Bai, and Ozan K. Tonguz. "Dynamics of Network Connectivity in Urban Vehicular Networks." IEEE Journal on Selected Areas in Communications 29, no. 3 (March 2011): 515–33. http://dx.doi.org/10.1109/jsac.2011.110303.

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van der Schaft, A. J., S. Rao, and B. Jayawardhana. "A network dynamics approach to chemical reaction networks." International Journal of Control 89, no. 4 (October 15, 2015): 731–45. http://dx.doi.org/10.1080/00207179.2015.1095353.

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Journal articles on the topic 'Dynamics network'

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