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Zeitschriftenartikel zum Thema „Network“

Autor: Grafiati

Veröffentlicht am 4. Juni 2021

Zuletzt aktualisiert am 25. Juli 2025

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1

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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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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3

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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4

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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5

Liu, Wen Wei, and Dan Wang. "On Link Density and Network Synchronization in Scale-Free Network." Applied Mechanics and Materials 380-384 (August 2013): 2276–79. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.2276.

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The relations between link density and network synchronizability based on scale-free weighted networks is investigated. In this work, it shows that synchronizability of networks Type I decrease along with the increases of link density, when the netwrok size is fixed. While the synchronizability of networks Type II is remarkable decreased by enhancing the link density with different network size.

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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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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

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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9

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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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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11

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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12

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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13

HAN, Shuangshuang, Feiyue WANG, Xiang CHENG, Xiaoya HU, Jian YANG, and Liuqing YANG. "Parallel networks and network softwarization: a novel network architecture." SCIENTIA SINICA Informationis 47, no. 7 (2017): 811. http://dx.doi.org/10.1360/n112016-00047.

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14

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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Lundberg, Heléne, and Christina Öberg. "A network of networks: knowledge development across network boundaries." Journal of Business & Industrial Marketing 40, no. 13 (2025): 77–86. https://doi.org/10.1108/jbim-07-2024-0502.

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Purpose The purpose of this paper is to examine knowledge development across network boundaries. Design/methodology/approach The empirical part of this paper is based on a longitudinal case study of a Swedish regional strategic network (RSN) in a sparely populated area. Data was collected through interviews, websites and social media. The case focuses on the creation of sub-networks designed to improve collaboration among different experts. The paper analyses how knowledge evolved across these sub-networks, the RSN and social and business networks. Findings The case study demonstrates how RSN

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16

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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17

Koam, Ali N. A., Ali Ahmad, and Yasir Ahmad. "Computation of reverse degree-based topological indices of hex-derived networks." AIMS Mathematics 6, no. 10 (2021): 11330–45. http://dx.doi.org/10.3934/math.2021658.

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<abstract><p>Network theory gives an approach to show huge and complex frameworks through a complete arrangement of logical devices. A network is made is made of vertices and edges, where the degree of a vertex refers to the number of joined edges. The degree appropriation of a network represents the likelihood of every vertex having a particular degree and shows significant worldwide network properties. Network theory has applications in many disciplines like basic sciences, computer science, engineering, medical, business, public health and sociology. There are some important net

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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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19

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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20

Jia, Wenjie, and Tao Jiang. "Information-defined networks: A communication network approach for network studies." China Communications 18, no. 7 (2021): 197–210. http://dx.doi.org/10.23919/jcc.2021.07.016.

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21

Kwon, Minhae, and Hyunggon Park. "Network Coding Based Evolutionary Network Formation for Dynamic Wireless Networks." IEEE Transactions on Mobile Computing 18, no. 6 (2019): 1316–29. http://dx.doi.org/10.1109/tmc.2018.2861001.

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22

Yao, Hongyi, Sidharth Jaggi, and Minghua Chen. "Passive Network Tomography for Erroneous Networks: A Network Coding Approach." IEEE Transactions on Information Theory 58, no. 9 (2012): 5922–40. http://dx.doi.org/10.1109/tit.2012.2204532.

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23

Kwon, Minhae, and Hyunggon Park. "Distributed Network Formation Strategy for Network Coding Based Wireless Networks." IEEE Signal Processing Letters 24, no. 4 (2017): 432–36. http://dx.doi.org/10.1109/lsp.2017.2670645.

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24

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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25

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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26

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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27

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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28

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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29

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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30

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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31

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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32

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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33

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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34

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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35

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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36

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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37

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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38

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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<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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Alic, Kemal, Mihael Mohorcic, and Ales Svigelj. "Network and Traffic Design Aspects in Network-Coding-Enabled Wireless Networks." International Journal of Computers Communications & Control 14, no. 3 (2019): 293–310. http://dx.doi.org/10.15837/ijccc.2019.3.3477.

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Practical experience of using opportunistic network coding has already been gained in several real network deployments, indicating the influence of some of the fundamental characteristics of the network and the traffic load. However, these aspects have not been systematically investigated in the scope of the construction of efficient and robust large-scale network-coding-enabled wireless mesh networks. In this paper we focus on these aspects using an example of two opportunistic networkcoding procedures: the well-known COPE and the Bearing Opportunistic Network coding (BON). In addition, the d

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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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41

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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42

Hrabcak, David, Lubomir Dobos, Jan Papaj, and Lubos Ovsenik. "Multilayered Network Model for Mobile Network Infrastructure Disruption." Sensors 20, no. 19 (2020): 5491. http://dx.doi.org/10.3390/s20195491.

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In this paper, the novel study of the multilayered network model for the disrupted infrastructure of the 5G mobile network is introduced. The aim of this study is to present the new way of incorporating different types of networks, such as Wireless Sensor Networks (WSN), Mobile Ad-Hoc Networks (MANET), and DRONET Networks into one fully functional multilayered network. The proposed multilayered network model also presents the resilient way to deal with infrastructure disruption due to different reasons, such as disaster scenarios or malicious actions. In the near future, new network technologi

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43

Todd, Brody, Abiose Ibigbami, and John Doucette. "Survivable Network Design and Optimization with Network Families." Journal of Computer Networks and Communications 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/940130.

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In modeling communication networks for simulation of survivability schemes, one goal is often to implement these schemes across varying degrees of nodal connectivity to get unbiased performance results. Abstractions of real networks, simple random networks, and families of networks are the most common categories of these sample networks. This paper looks at how using the network family concept provides a solid unbiased foundation to compare different network protection models. The network family provides an advantage over random networks by requiring one solution per average nodal degree, as o

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Boroojeni, Asma Azizi, Jeremy Dewar, Tong Wu, and James M. Hyman. "Generating bipartite networks with a prescribed joint degree distribution." Journal of Complex Networks 5, no. 6 (2017): 839–57. http://dx.doi.org/10.1093/comnet/cnx014.

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Abstract We describe a class of new algorithms to construct bipartite networks that preserves a prescribed degree and joint-degree (degree–degree) distribution of the nodes. Bipartite networks are graphs that can represent real-world interactions between two disjoint sets, such as actor–movie networks, author–article networks, co-occurrence networks and heterosexual partnership networks. Often there is a strong correlation between the degree of a node and the degrees of the neighbours of that node that must be preserved when generating a network that reflects the structure of the underling sys

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Usui, Shohei, Fujio Toriumi, Masato Matsuo, Takatsugu Hirayama, and Kenji Mase. "Greedy Network Growth Model of Social Network Service." Journal of Advanced Computational Intelligence and Intelligent Informatics 18, no. 4 (2014): 590–97. http://dx.doi.org/10.20965/jaciii.2014.p0590.

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As new network communication tools are developed, social network services (SNS) such as Facebook and Twitter are becoming part of a social phenomenon globally impacting on society. Many researchers are therefore studying the structure of relationship networks among users. We propose a greedy network growth model that appropriately increases nodes and links while automatically reproducing the target network. We handle a wide range of networks with high expressive ability. Results of experiments showed that we accurately reproduced 92.4% of 189 target networks from real services. The model also

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46

Qi, Shengyuan, Lin Yang, Linru Ma, Shanqing Jiang, and Guang Cheng. "Dual-Network Layered Network: A Method to Improve Reliability, Security, and Network Efficiency in Distributed Heterogeneous Network Transmission." Electronics 13, no. 23 (2024): 4749. https://doi.org/10.3390/electronics13234749.

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This article delves into the routing architecture and reliable transmission service framework of dual-network layered networks, with a focus on analyzing their core design ideas and implementation strategies. In the context of increasing network complexity today, traditional single-network architectures are unable to meet diverse application needs. Therefore, dual-network layered networks, as an innovative solution, are gradually receiving attention from both academia and industry. This article first analyzes the key technical elements in the dual-network layered network architecture, includin

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Xiao, Wen Hong, and Xiang Dong Cai. "A Novel Wireless Sensor Network Model Based on Complex Network Theory." Advanced Materials Research 546-547 (July 2012): 1276–82. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.1276.

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The key issue of wireless sensor networks is to balance the energy costs of the entire network, to enhance the robustness of the entire sensor network. Sensor networks as a special kind of complex network, in particular, environmental constraints, and more from the traditional complex networks, such as Internet networks, ecological networks, social networks, is to introduce a way of wireless sensor networks way of complex networks theory and analytical method, the key lies in, which is a successful model of complex network theory and analysis methods, more suitable for the application of wirel

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Zhang, Lei, Feng Qian, Jie Chen, and Shu Zhao. "An Unsupervised Rapid Network Alignment Framework via Network Coarsening." Mathematics 11, no. 3 (2023): 573. http://dx.doi.org/10.3390/math11030573.

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Network alignment aims to identify the correspondence of nodes between two or more networks. It is the cornerstone of many network mining tasks, such as cross-platform recommendation and cross-network data aggregation. Recently, with the development of network representation learning techniques, researchers have proposed many embedding-based network alignment methods. The effect is better than traditional methods. However, several issues and challenges remain for network alignment tasks, such as lack of labeled data, mapping across network embedding spaces, and computational efficiency. Based

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Zakiya Manzoor Khan, Et al. "Network Intrusion Detection Using Autoencode Neural Network." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10 (2023): 1678–88. http://dx.doi.org/10.17762/ijritcc.v11i10.8739.

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In today's interconnected digital landscape, safeguarding computer networks against unauthorized access and cyber threats is of paramount importance. NIDS play a crucial role in identifying and mitigating potential security breaches. This research paper explores the application of autoencoder neural networks, a subset of deep learning techniques, in the realm of Network Intrusion Detection.Autoencoder neural networks are known for their ability to learn and represent data in a compressed, low-dimensional form. This study investigates their potential in modeling network traffic patterns and ide

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Jiban, Jyoti Panda, and Aruna Tripathy Dr. "AI Based Network Slicing In 5G Network." Journal of VLSI Design and its Advancement 5, no. 2 (2022): 1–9. https://doi.org/10.5281/zenodo.6966985.

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<em>5g is the future of mobile communications. Upcoming networks needed high quality standards with reliability and very low latency. The main feature of upgrading into 5g networks is to have very high end user connectivity. Optimizing network slicing can be more helpful to achieving the remarkable performances in 5g networks. Network slicing divides the physical network into several logical networks in order to support the variety of developing applications with higher performance and flexibility needs. Recently data driven decisions are more efficient and useful for different industries, the

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