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Journal articles on the topic 'Deterministic networks'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 February 2022

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1

Barrière, L., F. Comellas, C. Dalfó, and M. A. Fiol. "Deterministic hierarchical networks." Journal of Physics A: Mathematical and Theoretical 49, no. 22 (May 3, 2016): 225202. http://dx.doi.org/10.1088/1751-8113/49/22/225202.

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2

Li, Xin-Feng, Zhe-Ming Lu, and Hui Li. "Controllability of deterministic complex networks." International Journal of Modern Physics C 26, no. 03 (February 25, 2015): 1550028. http://dx.doi.org/10.1142/s012918311550028x.

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Recently, the controllability of complex networks has attracted wide attention of researchers. The main contribution comes from Liu et al. who proposed the structural controllability as an analytical framework for making predictions regarding the control of directed networks in Nature. Since then, the controllability of many model and real networks has been deeply investigated except deterministic complex networks. In this paper, we focus on studying the controllability of deterministic complex networks. We examine six typical deterministic networks, the simulation results show that the minimum number of driver nodes grows linearly with network size. When the network size is large enough, the controllability approximates to a constant not more than 0.4, indicating that the deterministic networks are relatively easy to control. Furthermore, we investigate the characteristics of driver nodes in deterministic complex networks, finding that the driver nodes tend to avoid high degree nodes but to have high clustering coefficients.

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3

Benzaoui, Nihel, Mijail Szczerban Gonzalez, Jose Manuel Estaran, Haik Mardoyan, Wolfram Lautenschlaeger, Ulrich Gebhard, Lars Dembeck, Sebastien Bigo, and Yvan Pointurier. "Deterministic Dynamic Networks (DDN)." Journal of Lightwave Technology 37, no. 14 (July 15, 2019): 3465–74. http://dx.doi.org/10.1109/jlt.2019.2917280.

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4

Barabási, Albert-László, Erzsébet Ravasz, and Tamás Vicsek. "Deterministic scale-free networks." Physica A: Statistical Mechanics and its Applications 299, no. 3-4 (October 2001): 559–64. http://dx.doi.org/10.1016/s0378-4371(01)00369-7.

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5

Comellas, Francesc, and Michael Sampels. "Deterministic small-world networks." Physica A: Statistical Mechanics and its Applications 309, no. 1-2 (June 2002): 231–35. http://dx.doi.org/10.1016/s0378-4371(02)00741-0.

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6

Xing, Changming, Lin Yang, and Jun Ma. "A deterministic pseudo-fractal networks with time-delay." International Journal of Modern Physics B 29, no. 22 (September 7, 2015): 1550155. http://dx.doi.org/10.1142/s0217979215501556.

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In this paper, inspired by the pseudo-fractal networks (PFN) and the delayed pseudo-fractal networks (DPFN), we present a novel delayed pseudo-fractal networks model, denoted by NDPFN. Different from the generation algorithm of those two networks, every edge of the novel model has a time-delay to generate new nodes after producing one node. We derive exactly the main structural properties of the novel networks: degree distribution, clustering coefficient, diameter and average path length. Analytical results show that the novel networks have small-world effect and scale-free topology. Comparing topological parameters of these three networks, we find that the degree exponent of the novel networks is the largest while the clustering coefficient and the average path length are the smallest. It means that this kind of delay could weaken the heterogeneity and the small-world features of the network. Particularly, the delay effect in the NDPFN is contrary to that in the DPFN, which illustrates the variety of delay method could produce different effects on the network structure. These present findings may be helpful for a deeper understanding of the time-delay influence on the network topology.

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7

Ma, Y., X. Jiang, M. Li, and Z. Zheng. "Trapping on Deterministic Multiplex Networks." Acta Physica Polonica B 46, no. 4 (2015): 789. http://dx.doi.org/10.5506/aphyspolb.46.789.

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8

Ciszak, M., and R. Meucci. "Spontaneous Transitions in Deterministic Networks." Acta Physica Polonica B 45, no. 6 (2014): 1157. http://dx.doi.org/10.5506/aphyspolb.45.1157.

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9

Roy, Saptarshi, Titas Chanda, Tamoghna Das, Aditi Sen(De), and Ujjwal Sen. "Deterministic quantum dense coding networks." Physics Letters A 382, no. 26 (July 2018): 1709–15. http://dx.doi.org/10.1016/j.physleta.2018.04.033.

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10

Comellas, Francesc, Javier Ozón, and Joseph G. Peters. "Deterministic small-world communication networks." Information Processing Letters 76, no. 1-2 (November 2000): 83–90. http://dx.doi.org/10.1016/s0020-0190(00)00118-6.

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11

Czumaj, Artur, and Peter Davies. "Deterministic Communication in Radio Networks." SIAM Journal on Computing 47, no. 1 (January 2018): 218–40. http://dx.doi.org/10.1137/17m1111322.

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12

Mateescu, Robert, and Rina Dechter. "Mixed deterministic and probabilistic networks." Annals of Mathematics and Artificial Intelligence 54, no. 1-3 (November 2008): 3–51. http://dx.doi.org/10.1007/s10472-009-9132-y.

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13

Doty, David, and Monir Hajiaghayi. "Leaderless deterministic chemical reaction networks." Natural Computing 14, no. 2 (June 10, 2014): 213–23. http://dx.doi.org/10.1007/s11047-014-9435-8.

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14

JIANG, PIN-QUN, BING-HONG WANG, SHOU-LIANG BU, QING-HUA XIA, and XIAO-SHU LUO. "HYPERCHAOTIC SYNCHRONIZATION IN DETERMINISTIC SMALL-WORLD DYNAMICAL NETWORKS." International Journal of Modern Physics B 18, no. 17n19 (July 30, 2004): 2674–79. http://dx.doi.org/10.1142/s0217979204025890.

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In this paper, hyperchaotic synchronization in a network of continuous-time dynamical systems with small-world connections is investigated. The small-world network is obtained by selecting a part of nodes to be hubs and then using a globally coupled network to interconnect these hubs in an originally nearest-neighbor coupled network. We show that, the deterministic small-world dynamical network will also synchronize when the maximal Lyapunov exponent of the self-feedback system of single node is equated to, even great than zero. This explains why many real-world complex networks exhibit strong tendency toward synchronization even with a relatively weak coupling. Our study may shed some new light on synchronization phenomena in real complex networks.

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15

de Almeida Rego, Joilson Batista, Allan de Medeiros Martins, and Evandro de B. Costa. "Deterministic System Identification Using RBF Networks." Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/432593.

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This paper presents an artificial intelligence application using a nonconventional mathematical tool: the radial basis function (RBF) networks, aiming to identify the current plant of an induction motor or other nonlinear systems. Here, the objective is to present the RBF response to different nonlinear systems and analyze the obtained results. A RBF network is trained and simulated in order to obtain the dynamical solution with basin of attraction and equilibrium point for known and unknown system and establish a relationship between these dynamical systems and the RBF response. On the basis of several examples, the results indicating the effectiveness of this approach are demonstrated.

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16

de Bruin, Arie, and Shan-Hwei Nienhuys-Cheng. "Linear dynamic Kahn networks are deterministic." Theoretical Computer Science 195, no. 1 (March 1998): 3–32. http://dx.doi.org/10.1016/s0304-3975(97)00156-4.

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17

Kutten, Shay, David Peleg, and Uzi Vishkin. "Deterministic Resource Discovery in Distributed Networks." Theory of Computing Systems 36, no. 5 (August 6, 2003): 479–95. http://dx.doi.org/10.1007/s00224-003-1084-8.

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18

Gąsieniec, Leszek, Evangelos Kranakis, Andrzej Pelc, and Qin Xin. "Deterministic M2M multicast in radio networks." Theoretical Computer Science 362, no. 1-3 (October 2006): 196–206. http://dx.doi.org/10.1016/j.tcs.2006.06.017.

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19

Cicalese, Ferdinando, Fredrik Manne, and Qin Xin. "Faster Deterministic Communication in Radio Networks." Algorithmica 54, no. 2 (December 4, 2007): 226–42. http://dx.doi.org/10.1007/s00453-007-9136-0.

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20

Borsotti, Angelo, Luca Breveglieri, Stefano Crespi Reghizzi, and Angelo Morzenti. "Fast deterministic parsers for transition networks." Acta Informatica 55, no. 7 (November 4, 2017): 547–74. http://dx.doi.org/10.1007/s00236-017-0308-3.

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21

Grobler, M. J., and A. S. J. Helberg. "Identifying Opportunities for Deterministic Network Coding in Wireless Mesh Networks." SAIEE Africa Research Journal 102, no. 1 (March 2011): 8–15. http://dx.doi.org/10.23919/saiee.2011.8531563.

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22

Baldi, Mario, and Andrea Vesco. "Blocking Probability in Pipeline Forwarding Networks." Journal of Communications Software and Systems 10, no. 1 (March 21, 2014): 30. http://dx.doi.org/10.24138/jcomss.v10i1.138.

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As multimedia communications continue to grow steadily on the Internet, pipeline forwarding (PF) of packets provides a scalable solution for engineering delay-sensitive traffic while guaranteeing deterministic Quality of Service (QoS) with high resource utilization. In PF networks resource reservation, while ensuring deterministic QoS on a per-flow basis, can result in a not null blocking probability. A reservation request may fails due to enough resources being available but not during the proper time frames. This work analyses blocking probability of reservation requests since it affects the capability of utilizing network resources to carry traffic with deterministic QoS. The blocking probability and, consequently, the achievable network utilization are analytically derived on general topology PF networks as function of the traffic intensity given the traffic matrix and the network routing. The correctness of the blocking models is also assessed by simulation in different scenarios. This work represent a valuable contribution over previous analytical models of the blocking probability as their application to real size scenarios is impractical due to their computation complexity.

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23

Antonucci, Alessandro. "Modeling Deterministic Equations in Discrete Bayesian Networks by Credal Networks." Journal of Physics: Conference Series 1065 (August 2018): 212014. http://dx.doi.org/10.1088/1742-6596/1065/21/212014.

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24

Liao, Yunhua, Mohamed Maama, and M. A. Aziz-Alaoui. "Optimal networks for exact controllability." International Journal of Modern Physics C 31, no. 10 (August 20, 2020): 2050144. http://dx.doi.org/10.1142/s0129183120501442.

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The exact controllability can be mapped to the problem of maximum algebraic multiplicity of all eigenvalues. In this paper, we focus on the exact controllability of deterministic complex networks. First, we explore the eigenvalues of two famous networks, i.e. the comb-of-comb network and the Farey graph. Due to their special structure, we find that the eigenvalues of each network are mutually distinct, showing that these two networks are optimal networks with respect to exact controllability. Second, we study how to optimize the exact controllability of a deterministic network. Based on the spectral graph theory, we find that reducing the order of duplicate sets or co-duplicate sets which are two special vertex subsets can decrease greatly the exact controllability. This result provides an answer to an open problem of Li et al. [X. F. Li, Z. M. Lu and H. Li, Int. J. Mod. Phys. C 26, 1550028 (2015)]. Finally, we discuss the relation between the topological structure and the multiplicity of two special eigenvalues and the computational complexity of our method.

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25

ZHU, LING-ZAN, BEI-BEI YIN, LEI ZHAO, and KAI-YUAN CAI. "SCALE-FREE NETWORKS CAN BE LINEAR-WORLD." International Journal of Modern Physics B 25, no. 32 (December 30, 2011): 4593–603. http://dx.doi.org/10.1142/s0217979211059206.

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It was generally believed that scale-free networks would be small-world. In this paper, two models, named Model A and Model B, are proposed to show that certain scale-free networks can be linear-world instead of small-world. By linear-world, it means that the average path length L of the network grows linearly with the total number of nodes N, i.e., L~N. Model A generates a deterministic scale-free network with high assortativity and numerical simulations demonstrate that the network is linear-world when it satisfies degree exponent λ>1. Model B constructs a partially deterministic scale-free network, which is connected by identical small scale-free networks following certain rules. Analytical arguments and numerical simulations both yield L~N which suggests that it is also linear-world. It is further discussed in this paper that the network generated by Model Bcould be either correlated or uncorrelated. This suggests that, inconsistent with the results in related works, uncorrelated scale-free networks can also be linear-world.

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26

Lee, Jay Yoon, Sanket Vaibhav Mehta, Michael Wick, Jean-Baptiste Tristan, and Jaime Carbonell. "Gradient-Based Inference for Networks with Output Constraints." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4147–54. http://dx.doi.org/10.1609/aaai.v33i01.33014147.

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Practitioners apply neural networks to increasingly complex problems in natural language processing, such as syntactic parsing and semantic role labeling that have rich output structures. Many such structured-prediction problems require deterministic constraints on the output values; for example, in sequence-to-sequence syntactic parsing, we require that the sequential outputs encode valid trees. While hidden units might capture such properties, the network is not always able to learn such constraints from the training data alone, and practitioners must then resort to post-processing. In this paper, we present an inference method for neural networks that enforces deterministic constraints on outputs without performing rule-based post-processing or expensive discrete search. Instead, in the spirit of gradient-based training, we enforce constraints with gradient-based inference (GBI): for each input at test-time, we nudge continuous model weights until the network’s unconstrained inference procedure generates an output that satisfies the constraints. We study the efficacy of GBI on three tasks with hard constraints: semantic role labeling, syntactic parsing, and sequence transduction. In each case, the algorithm not only satisfies constraints, but improves accuracy, even when the underlying network is stateof-the-art.

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27

Asai, Yoshiyuki, Apratim Guha, and Alessandro E. P. Villa. "Deterministic neural dynamics transmitted through neural networks." Neural Networks 21, no. 6 (August 2008): 799–809. http://dx.doi.org/10.1016/j.neunet.2008.06.014.

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28

Manjunath, B. S., T. Simchony, and R. Chellappa. "Stochastic and deterministic networks for texture segmentation." IEEE Transactions on Acoustics, Speech, and Signal Processing 38, no. 6 (June 1990): 1039–49. http://dx.doi.org/10.1109/29.56064.

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29

Greil, Florian, Barbara Drossel, and Joost Sattler. "Critical Kauffman networks under deterministic asynchronous update." New Journal of Physics 9, no. 10 (October 18, 2007): 373. http://dx.doi.org/10.1088/1367-2630/9/10/373.

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30

Errera, M. R., and A. Bejan. "Deterministic Tree Networks for River Drainage Basins." Fractals 06, no. 03 (September 1998): 245–61. http://dx.doi.org/10.1142/s0218348x98000298.

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This paper shows that the dendritic patterns formed by low-resistance channels in a river drainage basin are reproducible and can be deduced from a single principle that acts at every step in the development of the pattern: the constrained minimization of global resistance in area-to-point flow. The river basin is modeled as a two-dimensional territory with Darcy flow through a saturated heterogeneous porous medium with uniform flow addition per unit area. From one step to the next, small elements of the porous medium are dislodged and removed in ways that minimize the global flow resistance. The removed elements are replaced by channels with lower flow resistance. The channels form a dendritic pattern that is deterministic, not random. The finest details of this structure are sensitive to internal properties and external forcing, i.e. variations in the local properties of the flow medium, and the manner in which the total area-to-point flow rate varies as the structure develops. Remarkably insensitive to such effects are the basic type and rough size of the flow structure (channels versus no channels, dendrite, number of branches) and the minimized global resistance to flow.

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31

Seok-Kyu Kweon and K. G. Shin. "Providing deterministic delay guarantees in ATM networks." IEEE/ACM Transactions on Networking 6, no. 6 (1998): 838–50. http://dx.doi.org/10.1109/90.748093.

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32

Zhang, Yichao, Zhongzhi Zhang, Shuigeng Zhou, and Jihong Guan. "Deterministic weighted scale-free small-world networks." Physica A: Statistical Mechanics and its Applications 389, no. 16 (August 2010): 3316–24. http://dx.doi.org/10.1016/j.physa.2010.04.003.

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33

Even, Guy, Moti Medina, Gregor Schaffrath, and Stefan Schmid. "Competitive and deterministic embeddings of virtual networks." Theoretical Computer Science 496 (July 2013): 184–94. http://dx.doi.org/10.1016/j.tcs.2012.10.036.

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34

Delaët, Sylvie, Partha Sarathi Mandal, Mariusz A. Rokicki, and Sébastien Tixeuil. "Deterministic secure positioning in wireless sensor networks." Theoretical Computer Science 412, no. 35 (August 2011): 4471–81. http://dx.doi.org/10.1016/j.tcs.2011.04.010.

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35

Fernández Anta, Antonio, Miguel A. Mosteiro, and Christopher Thraves. "Deterministic recurrent communication in restricted Sensor Networks." Theoretical Computer Science 418 (February 2012): 37–47. http://dx.doi.org/10.1016/j.tcs.2011.10.018.

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36

Sanders, B. C., G. Gour, and D. A. Meyer. "Deterministic entanglement of assistance in quantum networks." Canadian Journal of Physics 84, no. 6-7 (January 15, 2006): 639–44. http://dx.doi.org/10.1139/p06-019.

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We present a powerful theorem for tripartite remote entanglement distribution protocols, which provides an operational interpretation of concurrence as a type of entanglement capacity, and we establish that concurrence of assistance, which we show is an entanglement monotone, identifies capabilities of and limitations to producing pure bipartite entangled states from pure tripartite entangled states. In addition, we show that, if concurrence of assistance for the pure tripartite state is at least as large as the concurrence of the desired pure bipartite state, then the former may be transformed to the latter via local operations and classical communication, and we calculate the maximum probability for this transformation when this condition is not met.PACS Nos.: 03.67.Mn, 03.67.Hk, 03.65.Ud

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37

Paschos, S. A., M. E. Anagnostou, and F. N. Afrati. "Deterministic access protocols for packet radio networks." International Journal of Digital & Analog Communication Systems 6, no. 3 (1993): 151–60. http://dx.doi.org/10.1002/dac.4510060306.

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38

Borregaard, Johannes, Anders Søndberg Sørensen, and Peter Lodahl. "Quantum Networks with Deterministic Spin–Photon Interfaces." Advanced Quantum Technologies 2, no. 5-6 (April 10, 2019): 1800091. http://dx.doi.org/10.1002/qute.201800091.

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39

Pelc, Andrzej. "Deterministic rendezvous in networks: A comprehensive survey." Networks 59, no. 3 (January 23, 2012): 331–47. http://dx.doi.org/10.1002/net.21453.

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40

Diehl, Michael R., Sophia N. Yaliraki, Robert A. Beckman, Mauricio Barahona, and James R. Heath. "Self-Assembled, Deterministic Carbon Nanotube Wiring Networks." Angewandte Chemie 114, no. 2 (January 18, 2002): 363–66. http://dx.doi.org/10.1002/1521-3757(20020118)114:2<363::aid-ange363>3.0.co;2-i.

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41

Yang, D., J. Shin, and C. Kim. "Deterministic rendezvous scheme in multichannel access networks." Electronics Letters 46, no. 20 (2010): 1402. http://dx.doi.org/10.1049/el.2010.1990.

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42

Bocquillon, Ronan, and Antoine Jouglet. "Robust routing in deterministic delay-tolerant networks." Computers & Operations Research 92 (April 2018): 77–86. http://dx.doi.org/10.1016/j.cor.2017.12.004.

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43

Chen, Ho-Lin, David Doty, and David Soloveichik. "Deterministic function computation with chemical reaction networks." Natural Computing 13, no. 4 (September 3, 2013): 517–34. http://dx.doi.org/10.1007/s11047-013-9393-6.

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44

Chlebus, Bogdan S., Leszek Gasieniec, Alan Gibbons, Andrzej Pelc, and Wojciech Rytter. "Deterministic broadcasting in ad hoc radio networks." Distributed Computing 15, no. 1 (January 1, 2002): 27–38. http://dx.doi.org/10.1007/s446-002-8028-1.

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45

Filipiak, Janusz. "Unloading of congestion in deterministic queueing networks." Optimal Control Applications and Methods 2, no. 1 (October 29, 2007): 35–45. http://dx.doi.org/10.1002/oca.4660020104.

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46

Manolios, Peter, and Robert Fanelli. "First-Order Recurrent Neural Networks and Deterministic Finite State Automata." Neural Computation 6, no. 6 (November 1994): 1155–73. http://dx.doi.org/10.1162/neco.1994.6.6.1155.

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We examine the correspondence between first-order recurrent neural networks and deterministic finite state automata. We begin with the problem of inducing deterministic finite state automata from finite training sets, that include both positive and negative examples, an NP-hard problem (Angluin and Smith 1983). We use a neural network architecture with two recurrent layers, which we argue can approximate any discrete-time, time-invariant dynamic system, with computation of the full gradient during learning. The networks are trained to classify strings as belonging or not belonging to the grammar. The training sets used contain only short strings, and the sets are constructed in a way that does not require a priori knowledge of the grammar. After training, the networks are tested using various test sets with strings of length up to 1000, and are often able to correctly classify all the test strings. These results are comparable to those obtained with second-order networks (Giles et al. 1992; Watrous and Kuhn 1992a; Zeng et al. 1993). We observe that the networks emulate finite state automata, confirming the results of other authors, and we use a vector quantization algorithm to extract deterministic finite state automata after training and during testing of the networks, obtaining a table listing the start state, accept states, reject states, all transitions from the states, as well as some useful statistics. We examine the correspondence between finite state automata and neural networks in detail, showing two major stages in the learning process. To this end, we use a graphics module, which graphically depicts the states of the network during the learning and testing phases. We examine the networks' performance when tested on strings much longer than those in the training set, noting a measure based on clustering that is correlated to the stability of the networks. Finally, we observe that with sufficiently long training times, neural networks can become true finite state automata, due to the attractor structure of their dynamics.

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47

Király, Zoltán, and Erika R. Kovács. "Randomized and deterministic algorithms for network coding problems in wireless networks." Information Processing Letters 115, no. 4 (April 2015): 507–11. http://dx.doi.org/10.1016/j.ipl.2014.11.012.

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48

Zhao, Yao, Yan Chen, and David Bindel. "Towards deterministic network diagnosis." ACM SIGMETRICS Performance Evaluation Review 34, no. 1 (June 26, 2006): 387–88. http://dx.doi.org/10.1145/1140103.1140333.

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49

TAKEUCHI, NAOKI, and SATOSHI FUJITA. "Semi-Deterministic Construction of Scale-Free Networks with Designated Parameters." Journal of Interconnection Networks 18, no. 01 (March 2018): 1850001. http://dx.doi.org/10.1142/s0219265918500019.

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Scale-free networks have several favorable properties as the topology of interconnection networks such as the short diameter and the quick message propagation. In this paper, we propose a method to construct scale-free networks in a semi-deterministic manner. The proposed algorithm extends the Bulut's algorithm for constructing scale-free networks with designated minimum degree k and maximum degree m, in such a way that: (1) it determines the ideal number of edges derived from the ideal degree distribution; and (2) after connecting each new node to k existing nodes as in the Bulut’s algorithm, it adjusts the number of edges to the ideal value by conducting add/removal of edges. We prove that such an adjustment is always possible if the number of nodes in the network exceeds [Formula: see text]. The performance of the algorithm is experimentally evaluated.

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50

Wu, Weiqiang, Ning Huang, and Zhitao Wu. "Traffic chaotic dynamics modeling and analysis of deterministic network." Modern Physics Letters B 30, no. 18 (July 10, 2016): 1650285. http://dx.doi.org/10.1142/s0217984916502857.

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Network traffic is an important and direct acting factor of network reliability and performance. To understand the behaviors of network traffic, chaotic dynamics models were proposed and helped to analyze nondeterministic network a lot. The previous research thought that the chaotic dynamics behavior was caused by random factors, and the deterministic networks would not exhibit chaotic dynamics behavior because of lacking of random factors. In this paper, we first adopted chaos theory to analyze traffic data collected from a typical deterministic network testbed — avionics full duplex switched Ethernet (AFDX, a typical deterministic network) testbed, and found that the chaotic dynamics behavior also existed in deterministic network. Then in order to explore the chaos generating mechanism, we applied the mean field theory to construct the traffic dynamics equation (TDE) for deterministic network traffic modeling without any network random factors. Through studying the derived TDE, we proposed that chaotic dynamics was one of the nature properties of network traffic, and it also could be looked as the action effect of TDE control parameters. A network simulation was performed and the results verified that the network congestion resulted in the chaotic dynamics for a deterministic network, which was identical with expectation of TDE. Our research will be helpful to analyze the traffic complicated dynamics behavior for deterministic network and contribute to network reliability designing and analysis.

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