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Articoli di riviste sul tema "Networks"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 25 luglio 2025

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1

Kim, Junki. "Networks, Network Governance, and Networked Networks." International Review of Public Administration 11, no. 1 (2006): 19–34. http://dx.doi.org/10.1080/12294659.2006.10805075.

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2

PELLEGRINI, Lilla, Monica LEBA, and Alexandru IOVANOVICI. "CHARACTERIZATION OF URBAN TRANSPORTATION NETWORKS USING NETWORK MOTIFS." Acta Electrotechnica et Informatica 20, no. 4 (2020): 3–9. http://dx.doi.org/10.15546/aeei-2020-0019.

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We use tools and techniques specific to the field of complex networks analysis for the identification and extraction of key parameters which define ”good” patterns and practices for designing public transportation networks. Using network motifs we analyze a set of 18 cities using public data sets regarding the topology of network and discuss each of the identified motifs using the concepts and tools of urban planning.

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3

Shydlovskyi, P., and Ya Morozova. "FOREWORD: Network Approach for Studying the Prehistoric Networks." Vita Antiqua 1, no. 10 (2018): 6–12. http://dx.doi.org/10.37098/2519-4542-2018-1-10-6-12.

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4

THATIPAMULA RAJU, THATIPAMULA RAJU, and D. DEEPIKA RANI D. DEEPIKA RANI. "Achieving Network Level Privacy in Wireless Sensor Networks." International Journal of Scientific Research 2, no. 8 (2012): 183–87. http://dx.doi.org/10.15373/22778179/aug2013/61.

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5

Yiyuan Xie, Yiyuan Xie, and Zhu Yang Zhu Yang. "All-optical network interface from backbone networks to local area networks based on semiconductor optical amplifiers." Chinese Optics Letters 11, no. 11 (2013): 110605–8. http://dx.doi.org/10.3788/col201311.110605.

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6

Yi-Wei Ma, Yi-Wei Ma, Jiann-Liang Chen Yi-Wei Ma, Yu-Liang Tang Jiann-Liang Chen, and Kuan-Hung Lai Yu-Liang Tang. "Towards Adaptive Network Resource Orchestration for Cognitive Radio Networks." 網際網路技術學刊 23, no. 5 (2022): 1087–97. http://dx.doi.org/10.53106/160792642022092305017.

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<p>This work proposes an adaptive resource orchestration system for a Wireless Local Area Network (WLAN) that is based on the operating principle of Cognitive Radio (CR) technology. By collecting environmental parameters, including the retransmission rate and the channel occupancy rate, the proposed system has &ldquo;knowledge&rdquo; of overall transmission behavior and can regulate transmission resources. An Adaptive Connection Assignment (ACA) mechanism is proposed for end devices; it find out target end devices with poor transmission performance, analyzes their alternative Acc

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7

HRABCAK, David, and Lubomir DOBOS. "THE CONCEPT OF MULTILAYERED NETWORK MODEL FOR 5G NETWORKS." Acta Electrotechnica et Informatica 19, no. 3 (2019): 39–43. http://dx.doi.org/10.15546/aeei-2019-0022.

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8

Yan, Erjia, and Ying Ding. "Scholarly network similarities: How bibliographic coupling networks, citation networks, cocitation networks, topical networks, coauthorship networks, and coword networks relate to each other." Journal of the American Society for Information Science and Technology 63, no. 7 (2012): 1313–26. http://dx.doi.org/10.1002/asi.22680.

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9

Naranjo Orovio, Consuelo. "A network of networks." HAU: Journal of Ethnographic Theory 11, no. 1 (2021): 52–66. http://dx.doi.org/10.1086/713372.

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10

Iedema, Rick, Raj Verma, Sonia Wutzke, Nigel Lyons, and Brian McCaughan. "A network of networks." Journal of Health Organization and Management 31, no. 2 (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 achievemen

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11

Hepworth, H. Philip. "A network of networks." International Social Work 30, no. 1 (1987): 5–9. http://dx.doi.org/10.1177/002087288703000102.

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12

Fuengfusin, Ninnart, and Hakaru Tamukoh. "Network with Sub-Networks." Proceedings of International Conference on Artificial Life and Robotics 25 (January 13, 2020): 191–94. http://dx.doi.org/10.5954/icarob.2020.os20-2.

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13

Wang, Fei-Yue, Liuqing Yang, Xiang Cheng, Shuangshuang Han, and Jian Yang. "Network softwarization and parallel networks: beyond software-defined networks." IEEE Network 30, no. 4 (2016): 60–65. http://dx.doi.org/10.1109/mnet.2016.7513865.

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14

Walker, David M., Antoinette Tordesillas, Amy L. Rechenmacher, and Michael Small. "Multiscale resolution of networks of granular media network evolution—a network of networks." IEICE Proceeding Series 2 (March 17, 2014): 294–97. http://dx.doi.org/10.15248/proc.2.294.

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15

Hari, Parli Baijnath, and Shailendra Narayan Singh. "Security Attacks at MAC and Network Layer in Wireless Sensor Networks." Journal of Advanced Research in Dynamical and Control Systems 11, no. 12 (2019): 82–89. http://dx.doi.org/10.5373/jardcs/v11i12/20193215.

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16

Fu Jie Tey, Fu Jie Tey, Tin-Yu Wu Fu Jie Tey, Yueh Wu Tin-Yu Wu, and Jiann-Liang Chen Yueh Wu. "Generative Adversarial Network for Simulation of Load Balancing Optimization in Mobile Networks." 網際網路技術學刊 23, no. 2 (2022): 297–304. http://dx.doi.org/10.53106/160792642022032302010.

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Abstract (sommario):

<p>The commercial operation of 5G networks is almost ready to be launched, but problems related to wireless environment, load balancing for example, remain. Many load balancing methods have been proposed, but they were implemented in simulation environments that greatly differ from 5G networks. Current load balancing algorithms, on the other hand, focus on the selection of appropriate Wi-Fi or macro & small cells for Device to Device (D2D) communications, but Wi-Fi facilities and small cells are not available all the time. For this reason, we propose to use the macro cells that provi

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17

Sharma, Anita, and Prashant Salwan. "Network Matters! Revisiting Social Networks." Academy of Management Proceedings 2017, no. 1 (2017): 17317. http://dx.doi.org/10.5465/ambpp.2017.17317abstract.

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18

Fuengfusin, Ninnart, and Hakaru Tamukoh. "Convolutional Network with Sub-Networks." Proceedings of International Conference on Artificial Life and Robotics 26 (January 21, 2021): 6–9. http://dx.doi.org/10.5954/icarob.2021.os19-1.

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19

Hampson, J. "Urban network development [distribution networks]." Power Engineering Journal 15, no. 5 (2001): 224–32. http://dx.doi.org/10.1049/pe:20010504.

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20

Hirai, Shigeoki. "Multimedia Networks. Network and Telerobotics." Journal of the Robotics Society of Japan 15, no. 4 (1997): 516–19. http://dx.doi.org/10.7210/jrsj.15.516.

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21

Tizghadam, Ali, Weiwei Li, and Alberto Leon-Garcia. "Network criticality in vehicular networks." ACM SIGMETRICS Performance Evaluation Review 40, no. 3 (2012): 107–9. http://dx.doi.org/10.1145/2425248.2425278.

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22

Jiang, Chunxiao, Yan Chen, K. J. Ray Liu, and Yong Ren. "Network economics in cognitive networks." IEEE Communications Magazine 53, no. 5 (2015): 75–81. http://dx.doi.org/10.1109/mcom.2015.7105644.

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23

Kilkenny, Maureen, and Nerys Fuller Love. "Network analysis and business networks." International Journal of Entrepreneurship and Small Business 21, no. 3 (2014): 303. http://dx.doi.org/10.1504/ijesb.2014.060894.

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24

Havlin, S., D. Y. Kenett, A. Bashan, J. Gao, and H. E. Stanley. "Vulnerability of network of networks." European Physical Journal Special Topics 223, no. 11 (2014): 2087–106. http://dx.doi.org/10.1140/epjst/e2014-02251-6.

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25

Rutherford, Ian. "Network Theory and Theoric Networks." Mediterranean Historical Review 22, no. 1 (2007): 23–37. http://dx.doi.org/10.1080/09518960701538523.

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26

Wang, Shijun, Zhongbao Kou, and Changshui Zhang. "Network boosting on different networks." Physica A: Statistical Mechanics and its Applications 366 (July 2006): 561–70. http://dx.doi.org/10.1016/j.physa.2005.10.023.

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27

Huang, Gang, and Mark Tausig. "Network range in personal networks." Social Networks 12, no. 3 (1990): 261–68. http://dx.doi.org/10.1016/0378-8733(90)90009-x.

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28

Stalker, James, John Lasley, George Frederick, et al. "A Nationwide Network of Networks." Bulletin of the American Meteorological Society 94, no. 10 (2013): 1602–6. http://dx.doi.org/10.1175/1520-0477-94.10.1602.

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29

Hobbs, Linn W. "Network topology in aperiodic networks." Journal of Non-Crystalline Solids 192-193 (December 1995): 79–91. http://dx.doi.org/10.1016/0022-3093(95)00431-9.

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30

Durai C, Ramesh Babu, D. Madhivadhani, A. Sumathi, and Lily Saron Grace. "Graph neural networks for modeling ecological networks and food webs." Scientific Temper 16, no. 02 (2025): 3832–38. https://doi.org/10.58414/scientifictemper.2025.16.2.15.

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Abstract (sommario):

This paper investigates the application of Graph Neural Networks (GNNs) for modeling ecological networks and food webs. Using Python programming with libraries such as NumPy, Matplotlib, and NetworkX, random data generation is performed to simulate population sizes of different species within ecological networks. Various types of visualizations, including bar charts, line charts, and pie charts, are created to analyze population sizes, trends, and distribution of species. Additionally, NetworkX is employed to create graphical representations of ecological networks, including directed, spring l

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31

Provan, Keith G., and Juliann G. Sebastian. "Networks Within Networks: Service Link Overlap, Organizational Cliques, and Network Effectiveness." Academy of Management Journal 41, no. 4 (1998): 453–63. http://dx.doi.org/10.5465/257084.

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32

Cai, Han, Johan Chrisnata, Tuvi Etzion, Moshe Schwartz, and Antonia Wachter-Zeh. "Network-Coding Solutions for Minimal Combination Networks and Their Sub-Networks." IEEE Transactions on Information Theory 66, no. 11 (2020): 6786–98. http://dx.doi.org/10.1109/tit.2020.2995845.

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33

Nasiri, Ali Akbar, and Farnaz Derakhshan. "Assignment of Virtual Networks to Substrate Network for Software Defined Networks." International Journal of Cloud Applications and Computing 8, no. 4 (2018): 29–48. http://dx.doi.org/10.4018/ijcac.2018100103.

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Assigning multiple virtual network resources to physical network resources, called virtual network embedding (VNE), is known to be non-deterministic polynomial-time hard (NP-hard) problem. Currently software-defined networking (SDN) is gaining popularity in enterprise networks to improve the customizability and flexibility in network management service and reduced operational cost. A central controller in SDNs is an important factor that we need to take care of when we want to assign virtual networks to physical resources. In this work, we address virtual network embedding problems for SDNs. I

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34

Jadhav, Pranavati, and Dr Burra Vijaya Babu. "Detection of Community within Social Networks with Diverse Features of Network Analysis." Journal of Advanced Research in Dynamical and Control Systems 11, no. 12-SPECIAL ISSUE (2019): 366–71. http://dx.doi.org/10.5373/jardcs/v11sp12/20193232.

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35

Navghare, Tukaram, Aniket Muley, and Vinayak Jadhav. "Siamese Neural Networks for Kinship Prediction: A Deep Convolutional Neural Network Approach." Indian Journal Of Science And Technology 17, no. 4 (2024): 352–58. http://dx.doi.org/10.17485/ijst/v17i4.3018.

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36

Gao, Jianxi, Daqing Li, and Shlomo Havlin. "From a single network to a network of networks." National Science Review 1, no. 3 (2014): 346–56. http://dx.doi.org/10.1093/nsr/nwu020.

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Abstract Network science has attracted much attention in recent years due to its interdisciplinary applications. We witnessed the revolution of network science in 1998 and 1999 started with small-world and scale-free networks having now thousands of high-profile publications, and it seems that since 2010 studies of ‘network of networks’ (NON), sometimes called multilayer networks or multiplex, have attracted more and more attention. The analytic framework for NON yields a novel percolation law for n interdependent networks that shows that percolation theory of single networks studied extensive

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37

Mahat, Norpah, Nor Idayunie Nording, Jasmani Bidin, Suzanawati Abu Hasan, and Teoh Yeong Kin. "Artificial Neural Network (ANN) to Predict Mathematics Students’ Performance." Journal of Computing Research and Innovation 7, no. 1 (2022): 29–38. http://dx.doi.org/10.24191/jcrinn.v7i1.264.

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Predicting students’ academic performance is very essential to produce high-quality students. The main goal is to continuously help students to increase their ability in the learning process and to help educators as well in improving their teaching skills. Therefore, this study was conducted to predict mathematics students’ performance using Artificial Neural Network (ANN). The secondary data from 382 mathematics students from UCI Machine Learning Repository Data Sets used to train the neural networks. The neural network model built using nntool. Two inputs are used which are the first and the

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38

Lavrijssen, Saskia A. C. M., and Saskia A. C. M. Lavrijssen. "Networks on Track: From European Regulatory Networks to European Regulatory ‘Network Agencies’." Legal Issues of Economic Integration 36, Issue 1 (2009): 23–55. http://dx.doi.org/10.54648/leie2009003.

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Recent legal and political science literature has become increasingly critical on the accountability of what are generically referred to as European administrative networks, in which national administrative authorities cooperate with the EU institutions in a myriad of formal and informal ways in the development as well as the implementation of secondary EU legislation. This article deals with two specific regulatory networks – the European Energy Regulators Group (ERGEG) and the European Regulators Group for Communications Networks and Services (ERG). In 2007, the Commission tabled legislative

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39

Kawakubo, Hideko, Yusuke Matsui, Itaru Kushima, Norio Ozaki, and Teppei Shimamura. "A network of networks approach for modeling interconnected brain tissue-specific networks." Bioinformatics 35, no. 17 (2019): 3092–101. http://dx.doi.org/10.1093/bioinformatics/btz032.

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Abstract Motivation Recent sequence-based analyses have identified a lot of gene variants that may contribute to neurogenetic disorders such as autism spectrum disorder and schizophrenia. Several state-of-the-art network-based analyses have been proposed for mechanical understanding of genetic variants in neurogenetic disorders. However, these methods were mainly designed for modeling and analyzing single networks that do not interact with or depend on other networks, and thus cannot capture the properties between interdependent systems in brain-specific tissues, circuits and regions which are

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40

Rethemeyer, R. Karl. "MANAGING BY AND THROUGH NETWORKS: NETWORK MANAGEMENT, POLICY NETWORKS, AND THE INTERNET." Academy of Management Proceedings 2005, no. 1 (2005): F1—F6. http://dx.doi.org/10.5465/ambpp.2005.18783346.

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41

Sireesha, Manchoori, and Gobi N. "Computer Networks." International Journal of Research Publication and Reviews 5, no. 3 (2024): 1020–24. http://dx.doi.org/10.55248/gengpi.5.0324.0640.

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42

Karimi, Faezeh, David Green, Petr Matous, Manos Varvarigos, and Kaveh R. Khalilpour. "Network of networks: A bibliometric analysis." Physica D: Nonlinear Phenomena 421 (July 2021): 132889. http://dx.doi.org/10.1016/j.physd.2021.132889.

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43

Rahman, Atta-ur, Maqsood Mahmud, Tahir Iqbal, et al. "Network Anomaly Detection in 5G Networks." Mathematical Modelling of Engineering Problems 9, no. 2 (2022): 397–404. http://dx.doi.org/10.18280/mmep.090213.

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On the telecommunications front, 5G is the fifth-generation technology standard for broadband cellular networks, which is a replacement for the 4G networks used by most current phones. Hundreds of businesses, organizations, and governments suffer from cyberattacks that compromise sensitive information in which 5G is one of them. Those breaches of the data would not have occurred if there is a way to detect strange behaviors in a 5G network, and this is what this paper presenting. Network Anomaly Detection (NAD) in 5G is a way to observe the network constantly to detect any unusual behavior. Ho

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44

Kurokawa, Akira, Shuji Esaki, Atsushi Hiramatsu, and Hirofumi Horikoshi. "Network Technologies for Next Generation Networks." IEICE Communications Society Magazine 2010, no. 13 (2010): 13_10–13_21. http://dx.doi.org/10.1587/bplus.2010.13_10.

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45

Geraci, Salvatore, and Chiara Bodini. "Network of networks for immigrants' health." SALUTE E SOCIETÀ, no. 2 (May 2012): 148–61. http://dx.doi.org/10.3280/ses2011-002eng010.

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46

Whitmeyer, Joseph M. "Interest-Network Structures in Exchange Networks." Sociological Perspectives 42, no. 1 (1999): 23–47. http://dx.doi.org/10.2307/1389640.

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47

Nakatsu, Ryohei. "Multimedia Networks. Multimedia Network and Agent." Journal of the Robotics Society of Japan 15, no. 4 (1997): 507–11. http://dx.doi.org/10.7210/jrsj.15.507.

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48

Zavlanos, Michael M., Alejandro Ribeiro, and George J. Pappas. "Network Integrity in Mobile Robotic Networks." IEEE Transactions on Automatic Control 58, no. 1 (2013): 3–18. http://dx.doi.org/10.1109/tac.2012.2203215.

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49

Karimian, Pourya, Reza Rafie Borujeny, and Masoud Ardakani. "On Network Coding for Funnel Networks." IEEE Communications Letters 19, no. 11 (2015): 1897–900. http://dx.doi.org/10.1109/lcomm.2015.2477816.

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50

Chan, Vincent W. S. "Multi-Layer Network Research: Optical Networks." IEEE Communications Magazine 58, no. 9 (2020): 4. http://dx.doi.org/10.1109/mcom.2020.9214375.

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