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Journal articles on the topic 'Connected networks'

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Author: Grafiati

Published: 10 December 2022

Last updated: 30 January 2023

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1

Treur, Jan. "Analysis of a network’s asymptotic behavior via its structure involving its strongly connected components." Network Science 8, S1 (October 1, 2019): S82—S109. http://dx.doi.org/10.1017/nws.2019.24.

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AbstractIn this paper, it is addressed how network structure can be related to asymptotic network behavior. If such a relation is studied, that usually concerns only strongly connected networks and only linear functions describing the dynamics. In this paper, both conditions are generalized. A couple of general theorems is presented that relates asymptotic behavior of a network to the network’s structure characteristics. The network structure characteristics, on the one hand, concern the network’s strongly connected components and their mutual connections; this generalizes the condition of being strongly connected to a very general condition. On the other hand, the network structure characteristics considered generalize from linear functions to functions that are normalized, monotonic, and scalar-free, so that many nonlinear functions are also covered. Thus, the contributed theorems generalize the existing theorems on the relation between network structure and asymptotic network behavior addressing only specific cases such as acyclic networks, fully, and strongly connected networks, and theorems addressing only linear functions. This paper was invited as an extended (by more than 45%) version of a Complex Networks’18 conference paper. In the discussion section, the differences are explained in more detail.

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2

Stone, Tim. "Protecting connected transportation networks." Network Security 2018, no. 12 (December 2018): 8–10. http://dx.doi.org/10.1016/s1353-4858(18)30125-9.

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3

Dunbar, Robin. "Social networks: Getting connected." New Scientist 214, no. 2859 (April 2012): ii—iii. http://dx.doi.org/10.1016/s0262-4079(12)60855-0.

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4

Majetich, SA, AC Carter, R. D. McCullough, J. Seth, and J. A. Belot. "Connected CdSe nanocrystallite networks." Zeitschrift f�r Physik D Atoms, Molecules and Clusters 26, S1 (March 1993): 210–12. http://dx.doi.org/10.1007/bf01425667.

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5

Johnston, A. "Networks as Connected Contracts." Industrial Law Journal 41, no. 3 (September 1, 2012): 374–79. http://dx.doi.org/10.1093/indlaw/dws022.

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6

Quillinan, J. "The connected building [Internet connected building control networks]." IEE Review 51, no. 4 (April 1, 2005): 44–47. http://dx.doi.org/10.1049/ir:20050405.

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7

Bläser, Markus, Andreas Jakoby, Maciej Liskiewicz, and Bodo Manthey. "Private Computation: k-Connected versus 1-Connected Networks." Journal of Cryptology 19, no. 3 (May 27, 2005): 341–57. http://dx.doi.org/10.1007/s00145-005-0329-x.

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8

Firat, Mehmet. "Analysis of 3D Virtual Worlds as Connected Knowledge Networks." International Journal of Information and Education Technology 4, no. 2 (2014): 203–7. http://dx.doi.org/10.7763/ijiet.2014.v4.399.

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9

Buechel, Berno, and Tim Hellmann. "Under-connected and over-connected networks: the role of externalities in strategic network formation." Review of Economic Design 16, no. 1 (February 15, 2012): 71–87. http://dx.doi.org/10.1007/s10058-012-0114-x.

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10

Wu, Qingxiu, Zhanji Gui, Shuqing Li, and Jun Ou. "Directly Connected Convolutional Neural Networks." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 05 (January 3, 2018): 1859007. http://dx.doi.org/10.1142/s0218001418590073.

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Convolutional neural networks (CNNs) have better performance in feature extraction and classification. Most of the applications are based on a traditional structure of CNNs. However, due to the fixed structure, it may not be effective for large dataset which will spend much time for training. So, we use a new algorithm to optimize CNNs, called directly connected convolutional neural networks (DCCNNs). In DCCNNs, the down-sampling layer can directly connect the output layer with three-dimensional matrix operation, without full connection (i.e., matrix vectorization). Thus, DCCNNs have less weights and neurons than CNNs. We conduct the comparison experiments on five image databases: MNIST, COIL-20, AR, Extended Yale B, and ORL. The experiments show that the model has better recognition accuracy and faster convergence than CNNs. Furthermore, two applications (i.e., water quality evaluation and image classification) following the proposed concepts further confirm the generality and capability of DCCNNs.

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11

AGARWAL, NIKITA. "Inflation of strongly connected networks." Mathematical Proceedings of the Cambridge Philosophical Society 150, no. 2 (January 12, 2011): 367–84. http://dx.doi.org/10.1017/s0305004110000654.

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AbstractA coupled cell network is an inflation of if the dynamics of is embedded in as a quotient network. We give necessary and sufficient conditions for the existence of a strongly connected inflation of a strongly connected network. We provide a simple algorithm for the construction of a strongly connected inflation as a sequence of simple inflations.

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12

Rödseth, Öystein J. "Weighted multi-connected loop networks." Discrete Mathematics 148, no. 1-3 (January 1996): 161–73. http://dx.doi.org/10.1016/0012-365x(94)00239-f.

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13

Wang, Deqiang, and Lianchang Zhao. "The twisted-cube connected networks." Journal of Computer Science and Technology 14, no. 2 (March 1999): 181–87. http://dx.doi.org/10.1007/bf02946526.

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14

Tarapata, Zbigniew. "Modelling and analysis of transportation networks using complex networks: Poland case study." Archives of Transport 36, no. 4 (December 31, 2015): 55–65. http://dx.doi.org/10.5604/08669546.1185207.

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In the paper a theoretical bases and empirical results deal with analysis and modelling of transportation networks in Poland using complex networks have been presented. Properties of complex networks (Scale Free and Small World) and network's characteristic measures have been described. In this context, results of empirical researches connected with characteristics of passenger air links network, express railway links network (EuroCity and InterCity) and expressways/highways network in Poland have been given. For passenger air links network in Poland results are compared with the same networks in USA, China, India, Italy and Spain. In the conclusion some suggestions, observations and perspective dealing with complex network in transportation networks have been presented.

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15

Wong, W. K., Z. X. Guo, and S. Y. S. Leung. "Partially connected feedforward neural networks on Apollonian networks." Physica A: Statistical Mechanics and its Applications 389, no. 22 (November 2010): 5298–307. http://dx.doi.org/10.1016/j.physa.2010.06.061.

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16

Laws, C. N., and Y. C. Teh. "Alternative routeing in fully connected queueing networks." Advances in Applied Probability 32, no. 4 (December 2000): 962–82. http://dx.doi.org/10.1017/s0001867800010405.

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We consider a fully connected queueing network in which customers have one direct and many alternative routes through the network, and where customer routeing is dynamic. We obtain an asymptotically optimal routeing policy, taking the limit as the number of queues of the network increases. We observe that good policies route customers directly, unless there is a danger of servers becoming idle, in which case customers should be routed alternatively so as to avoid such idleness, and this leads to policies that perform well in moderate-sized networks.

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17

Poongodi, C., and A. M. Natarajan. "Optimized Replication Strategy for Intermittently Connected Mobile Networks." International Journal of Business Data Communications and Networking 8, no. 1 (January 2012): 1–18. http://dx.doi.org/10.4018/jbdcn.2012010101.

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Intermittently Connected Mobile Networks (ICMNs) are wireless networks where due to mobility of nodes and lack of connectivity, there may be disconnection among the nodes. Hence, the routing path from source to destination is not always available. In this case, Mobile Ad-hoc Network (MANET) protocols will not be utilized. In these networks, messages are to be flooded or multiple replications are needed to withstand the maximum delay and achieve the high delivery ratio. But multiple replication based protocols result in increased network overhead and high resource consumption because of uncontrolled replication. In this paper, the authors introduce a new simple scheme which applies knapsack policy based replication strategy in replicating the messages. The number of replication is reduced by appropriately selecting only limited messages based on the number of duplications of its own and its size. The messages are selected for forwarding to relay node based on the goodness of the relay node in contacting the destination and the buffer size of the relay node. Therefore, only limited messages will be replicated in the network and it will reduce the network overhead, resource consumption, delivery delay and increases the delivery ratio.

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18

SIPPER, MOSHE. "CLUSTER-DENSE NETWORKS." International Journal of Modern Physics C 19, no. 06 (June 2008): 939–46. http://dx.doi.org/10.1142/s0129183108012650.

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Small-world networks, exhibiting short nodal distances and high clustering, and scale-free networks, typified by a scale-free, power-law node-degree distribution, have been shown to be widespread both in natural and artificial systems. We propose a new type of network — cluster-dense network — characterized by multiple clusters that are highly intra-connected and sparsely inter-connected. Employing two graph-theoretic measures — local density and relative density — we demonstrate that such networks are prevalent in the world of networks.

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19

TAKABATAKE, TOSHINORI, TOMOKI NAKAMIGAWA, and HIDEO ITO. "CONNECTIVITY OF GENERALIZED HIERARCHICAL COMPLETELY-CONNECTED NETWORKS." Journal of Interconnection Networks 09, no. 01n02 (March 2008): 127–39. http://dx.doi.org/10.1142/s0219265908002199.

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As a network topology for a massively parallel computer system, Generalized Hierarchical Completely-Connected Networks (for short, HCC), which include conventional hierarchical networks, have been proposed. To apply the HCC to a parallel computer system effectively and to execute data processings on the HCC efficiently, the inherent fault-tolerant properties in HCC must be revealed. However, these properties have not been clarified enough. In this paper, node-connectivity is verified for HCC. Furthermore, the concept of block-connectivity related to node-connectivity of HCC is introduced, and fault-tolerance of HCC is discussed.

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20

Bhowmick, Sourav S. "How Connected Are Our Conference Review Boards?" ACM SIGMOD Record 51, no. 4 (January 9, 2023): 74–78. http://dx.doi.org/10.1145/3582302.3582324.

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Dense co-authorship network formed by the review board members of a conference may adversely impact the quality and integrity of the review process. In this report, we shed light on the topological characteristics of such networks for three major data management conference venues. Our results show all these venues give rise to dense networks with a large giant component. We advocate to rethink the traditional way review boards are formed to mitigate the emergence of dense networks.

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21

Wallace, Edward, Hamid Reza Maei, and Peter E. Latham. "Randomly Connected Networks Have Short Temporal Memory." Neural Computation 25, no. 6 (June 2013): 1408–39. http://dx.doi.org/10.1162/neco_a_00449.

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The brain is easily able to process and categorize complex time-varying signals. For example, the two sentences, “It is cold in London this time of year” and “It is hot in London this time of year,” have different meanings, even though the words hot and cold appear several seconds before the ends of the two sentences. Any network that can tell these sentences apart must therefore have a long temporal memory. In other words, the current state of the network must depend on events that happened several seconds ago. This is a difficult task, as neurons are dominated by relatively short time constants—tens to hundreds of milliseconds. Nevertheless, it was recently proposed that randomly connected networks could exhibit the long memories necessary for complex temporal processing. This is an attractive idea, both for its simplicity and because little tuning of recurrent synaptic weights is required. However, we show that when connectivity is high, as it is in the mammalian brain, randomly connected networks cannot exhibit temporal memory much longer than the time constants of their constituent neurons.

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22

Lee, I. Y. Y., and Sheng-De Wang. "Ring-connected networks and their relationship to cubical ring connected cycles and dynamic redundancy networks." IEEE Transactions on Parallel and Distributed Systems 6, no. 9 (1995): 988–96. http://dx.doi.org/10.1109/71.466635.

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23

ABDULLAH, W. A. T. WAN. "CROSS-CORRELATED MEMORIES IN COMPLETELY-CONNECTED NETWORKS." International Journal of Pattern Recognition and Artificial Intelligence 04, no. 01 (March 1990): 113–21. http://dx.doi.org/10.1142/s0218001490000083.

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The Hopfield model for associative recall in a massively connected binary network is reviewed. The problems involved in representation are pointed out. Learning of cross-correlations is introduced, and results of computer simulations are displayed. The relationship with least mean squares learning in feed-forward layered networks is pointed out, leading to the analogue of unlearning. Simulation results show the performance of such a system.

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24

Avedisov, Sergei S., and Gábor Orosz. "Analysis of connected vehicle networks using network-based perturbation techniques." Nonlinear Dynamics 89, no. 3 (May 8, 2017): 1651–72. http://dx.doi.org/10.1007/s11071-017-3541-y.

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25

Buttazzo, Giuseppe, and Filippo Santambrogio. "Asymptotical compliance optimization for connected networks." Networks & Heterogeneous Media 2, no. 4 (2007): 761–77. http://dx.doi.org/10.3934/nhm.2007.2.761.

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26

Babarczi, Peter, Janos Tapolcai, Alija Pasic, Lajos Ronyai, Erika R. Berczi-Kovacs, and Muriel Medard. "Diversity Coding in Two-Connected Networks." IEEE/ACM Transactions on Networking 25, no. 4 (August 2017): 2308–19. http://dx.doi.org/10.1109/tnet.2017.2684909.

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27

Lindsey, William C., and Jeng-Hong Chen. "Architectures for arbitrarily connected synchronization networks." Journal of Communications and Networks 1, no. 2 (June 1999): 89–98. http://dx.doi.org/10.1109/jcn.1999.6596752.

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28

Denic, Stojan, Orestis Georgiou, and Umberto Spagnolini. "Distributed Synchronization on Weakly Connected Networks." IEEE Communications Letters 21, no. 7 (July 2017): 1577–80. http://dx.doi.org/10.1109/lcomm.2017.2690284.

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29

Haas, Mary RC, Kellen Haley, Bella S. Nagappan, Felix Ankel, Anand Swaminathan, and Sally A. Santen. "The connected educator: personal learning networks." Clinical Teacher 17, no. 4 (February 24, 2020): 373–77. http://dx.doi.org/10.1111/tct.13146.

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30

Canning, A., and E. Gardner. "Partially connected models of neural networks." Journal of Physics A: Mathematical and General 21, no. 15 (August 7, 1988): 3275–84. http://dx.doi.org/10.1088/0305-4470/21/15/016.

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31

GIBBENS, R. J., and F. P. KELLY. "Dynamic Routing in Fully Connected Networks." IMA Journal of Mathematical Control and Information 7, no. 1 (1990): 77–111. http://dx.doi.org/10.1093/imamci/7.1.77.

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32

Neubauer, Nicolas, and Klaus Obermayer. "Hyperincident connected components of tagging networks." ACM SIGWEB Newsletter, Autumn (September 2009): 1–10. http://dx.doi.org/10.1145/1592394.1592398.

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33

Li, Guoqing, Meng Zhang, Jiaojie Li, Feng Lv, and Guodong Tong. "Efficient densely connected convolutional neural networks." Pattern Recognition 109 (January 2021): 107610. http://dx.doi.org/10.1016/j.patcog.2020.107610.

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34

Winter, Pawel, and Martin Zachariasen. "Two-connected Steiner networks: structural properties." Operations Research Letters 33, no. 4 (July 2005): 395–402. http://dx.doi.org/10.1016/j.orl.2004.07.010.

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35

Wang, Liejun, Shuli Cheng, Jiwei Qin, and Huanglu Wen. "Asymmetric convolution with densely connected networks." International Journal of Computing Science and Mathematics 12, no. 3 (2020): 274. http://dx.doi.org/10.1504/ijcsm.2020.10034030.

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36

Wang, Liejun, Huanglu Wen, Jiwei Qin, and Shuli Cheng. "Asymmetric convolution with densely connected networks." International Journal of Computing Science and Mathematics 12, no. 3 (2020): 274. http://dx.doi.org/10.1504/ijcsm.2020.111704.

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37

Krysta, Piotr. "Approximating minimum size {1,2}-connected networks." Discrete Applied Mathematics 125, no. 2-3 (February 2003): 267–88. http://dx.doi.org/10.1016/s0166-218x(02)00199-3.

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38

Kowald, Matthias, and Kay W. Axhausen. "Surveying data on connected personal networks." Travel Behaviour and Society 1, no. 2 (May 2014): 57–68. http://dx.doi.org/10.1016/j.tbs.2013.11.001.

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39

Hong, Zhen-Mu, and Jun-Ming Xu. "Vulnerability of super edge-connected networks." Theoretical Computer Science 520 (February 2014): 75–86. http://dx.doi.org/10.1016/j.tcs.2013.10.021.

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40

Srinivasan, Satish M., and Azad H. Azadmanesh. "Data aggregation in partially connected networks." Computer Communications 32, no. 4 (March 2009): 594–601. http://dx.doi.org/10.1016/j.comcom.2008.11.021.

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41

Goemans, Michel X., and Kalyan T. Talluri. "2-Change for k-connected networks." Operations Research Letters 10, no. 2 (March 1991): 113–17. http://dx.doi.org/10.1016/0167-6377(91)90096-8.

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42

Orange, Sébastien, and Nathalie Verdière. "Nonlinear synchronization on connected undirected networks." Nonlinear Dynamics 76, no. 1 (October 25, 2013): 47–55. http://dx.doi.org/10.1007/s11071-013-1108-0.

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43

Cheng, Yiping. "Backpropagation for Fully Connected Cascade Networks." Neural Processing Letters 46, no. 1 (January 25, 2017): 293–311. http://dx.doi.org/10.1007/s11063-017-9588-4.

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44

Tutanescu, I., E. Sofron, and M. Ali. "Security of internet-connected computer networks." International Journal of Internet Technology and Secured Transactions 2, no. 1/2 (2010): 109. http://dx.doi.org/10.1504/ijitst.2010.031474.

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45

Monma, Clyde L., Beth Spellman Munson, and William R. Pulleyblank. "Minimum-weight two-connected spanning networks." Mathematical Programming 46, no. 1-3 (January 1990): 153–71. http://dx.doi.org/10.1007/bf01585735.

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46

师, 海忠. "K Dodecahedron-Shi Connected Cycles Networks." Computer Science and Application 08, no. 06 (2018): 1013–26. http://dx.doi.org/10.12677/csa.2018.86113.

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47

Lindgren, Anders, Avri Doria, and Olov Schelén. "Probabilistic routing in intermittently connected networks." ACM SIGMOBILE Mobile Computing and Communications Review 7, no. 3 (July 2003): 19–20. http://dx.doi.org/10.1145/961268.961272.

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48

Ben-Ameur, Walid, and Makhlouf Hadji. "Steiner Networks with unicyclic connected components." Electronic Notes in Discrete Mathematics 36 (August 2010): 969–76. http://dx.doi.org/10.1016/j.endm.2010.05.123.

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49

Kornilovitch, P. E., R. N. Bicknell, and J. S. Yeo. "Fully-connected networks with local connections." Applied Physics A 95, no. 4 (February 19, 2009): 999–1004. http://dx.doi.org/10.1007/s00339-009-5124-3.

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50

Valet, Manon, Léa-Laetitia Pontani, Élie Wandersman, and Alexis Prevost. "Diffusion in Nanopore Connected Bilayer Networks." Biophysical Journal 116, no. 3 (February 2019): 41a. http://dx.doi.org/10.1016/j.bpj.2018.11.267.

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