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Articoli di riviste sul tema "Network"

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Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 7 luglio 2024

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1

PELLEGRINI, Lilla, Monica LEBA e Alexandru IOVANOVICI. "CHARACTERIZATION OF URBAN TRANSPORTATION NETWORKS USING NETWORK MOTIFS". Acta Electrotechnica et Informatica 20, n. 4 (21 gennaio 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., e Ya Morozova. "FOREWORD: Network Approach for Studying the Prehistoric Networks". Vita Antiqua 1, n. 10 (2018): 6–12. http://dx.doi.org/10.37098/2519-4542-2018-1-10-6-12.

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THATIPAMULA RAJU, THATIPAMULA RAJU, e D. DEEPIKA RANI D. DEEPIKA RANI. "Achieving Network Level Privacy in Wireless Sensor Networks". International Journal of Scientific Research 2, n. 8 (1 giugno 2012): 183–87. http://dx.doi.org/10.15373/22778179/aug2013/61.

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Mahat, Norpah, Nor Idayunie Nording, Jasmani Bidin, Suzanawati Abu Hasan e Teoh Yeong Kin. "Artificial Neural Network (ANN) to Predict Mathematics Students’ Performance". Journal of Computing Research and Innovation 7, n. 1 (30 marzo 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 second period grade while one target output is used which is the final grade. This study also aims to identify which training function is the best among three Feed-Forward Neural Networks known as Network1, Network2 and Network3. Three types of training functions have been selected in this study, which are Levenberg-Marquardt (TRAINLM), Gradient descent with momentum (TRAINGDM) and Gradient descent with adaptive learning rate (TRAINGDA). Each training function will be compared based on Performance value, correlation coefficient, gradient and epoch. MATLAB R2020a was used for data processing. The results show that the TRAINLM function is the most suitable function in predicting mathematics students’ performance because it has a higher correlation coefficient and a lower Performance value.

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LANDSPERGER, JOHANNES, PATRICK SPIETH e SVEN HEIDENREICH. "HOW NETWORK MANAGERS CONTRIBUTE TO INNOVATION NETWORK PERFORMANCE". International Journal of Innovation Management 16, n. 06 (30 novembre 2012): 1240009. http://dx.doi.org/10.1142/s1363919612400099.

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Innovation networks that aim at the joint development of products, services or processes represent a particular form of inter-organizational business networks. In order to yield useful results from these collaborations, networks need to be managed thoroughly. By appointing a dedicated network manager to administrate, coordinate, and regulate, the management of tasks is bundled and centralized within a single entity. However, to the best knowledge of the authors, no empirical research has yet been conducted, investigating the impact of a network manager's availability, relevance, and influence on network performance. Using the interaction-oriented network approach as conceptual foundation, we analyze network managers' direct and indirect influence on the network's relational and goal achievement performance. Our results suggest that a network manager enhances innovation network's core management functions, which in turn improve the relational performance (RP). Moreover, RP was found to significantly drive the goal achievement performance (GAP).

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Liu, Wen Wei, e Dan Wang. "On Link Density and Network Synchronization in Scale-Free Network". Applied Mechanics and Materials 380-384 (agosto 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 e Kuan-Hung Lai Yu-Liang Tang. "Towards Adaptive Network Resource Orchestration for Cognitive Radio Networks". 網際網路技術學刊 23, n. 5 (settembre 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 “knowledge” 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 Access Point (AP) availability and causes them to change connections to improve transmission performance. An Adaptive Channel Utilization (ACU) mechanism is designed for APs to identify a target AP that is suffering from interference, to analyze its alternative channel availability and to require it to change its working channel to improve transmission efficiency. Results of simulations of various scenarios indicate that the throughput of end devices is increased by 15 to 24%, the throughput of APs is increased by 6 to 47% and the retransmission rate of APs is reduced by 0.4 to 5.3%.</p> <p> </p>

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HRABCAK, David, e Lubomir DOBOS. "THE CONCEPT OF MULTILAYERED NETWORK MODEL FOR 5G NETWORKS". Acta Electrotechnica et Informatica 19, n. 3 (4 dicembre 2019): 39–43. http://dx.doi.org/10.15546/aeei-2019-0022.

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Naranjo Orovio, Consuelo. "A network of networks". HAU: Journal of Ethnographic Theory 11, n. 1 (1 marzo 2021): 52–66. http://dx.doi.org/10.1086/713372.

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Iedema, Rick, Raj Verma, Sonia Wutzke, Nigel Lyons e Brian McCaughan. "A network of networks". Journal of Health Organization and Management 31, n. 2 (10 aprile 2017): 223–36. http://dx.doi.org/10.1108/jhom-07-2016-0146.

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

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Hepworth, H. Philip. "A network of networks". International Social Work 30, n. 1 (gennaio 1987): 5–9. http://dx.doi.org/10.1177/002087288703000102.

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

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Hu, Ziping, Krishnaiyan Thulasiraman e Pramode K. Verma. "Complex Networks: Traffic Dynamics, Network Performance, and Network Structure". American Journal of Operations Research 03, n. 01 (2013): 187–95. http://dx.doi.org/10.4236/ajor.2013.31a018.

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HAN, Shuangshuang, Feiyue WANG, Xiang CHENG, Xiaoya HU, Jian YANG e Liuqing YANG. "Parallel networks and network softwarization: a novel network architecture". SCIENTIA SINICA Informationis 47, n. 7 (1 luglio 2017): 811. http://dx.doi.org/10.1360/n112016-00047.

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Gao, Jianxi, Daqing Li e Shlomo Havlin. "From a single network to a network of networks". National Science Review 1, n. 3 (16 luglio 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 extensively in physics and mathematics in the last 50 years is a specific limit of the rich and very different general case of n coupled networks. Since then, properties and dynamics of interdependent and interconnected networks have been studied extensively, and scientists are finding many interesting results and discovering many surprising phenomena. Because most natural and engineered systems are composed of multiple subsystems and layers of connectivity, it is important to consider these features in order to improve our understanding of such complex systems. Now the study of NON has become one of the important directions in network science. In this paper, we review recent studies on the new emerging area—NON. Due to the fast growth of this field, there are many definitions of different types of NON, such as interdependent networks, interconnected networks, multilayered networks, multiplex networks and many others. There exist many datasets that can be represented as NON, such as network of different transportation networks including flight networks, railway networks and road networks, network of ecological networks including species interacting networks and food webs, network of biological networks including gene regulation network, metabolic network and protein–protein interacting network, network of social networks and so on. Among them, many interdependent networks including critical infrastructures are embedded in space, introducing spatial constraints. Thus, we also review the progress on study of spatially embedded networks. As a result of spatial constraints, such interdependent networks exhibit extreme vulnerabilities compared with their non-embedded counterparts. Such studies help us to understand, realize and hopefully mitigate the increasing risk in NON.

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Kim, Junki. "Networks, Network Governance, and Networked Networks". International Review of Public Administration 11, n. 1 (luglio 2006): 19–34. http://dx.doi.org/10.1080/12294659.2006.10805075.

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Hari, Parli Baijnath, e Shailendra Narayan Singh. "Security Attacks at MAC and Network Layer in Wireless Sensor Networks". Journal of Advanced Research in Dynamical and Control Systems 11, n. 12 (20 dicembre 2019): 82–89. http://dx.doi.org/10.5373/jardcs/v11i12/20193215.

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

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Tarapata, Zbigniew. "Modelling and analysis of transportation networks using complex networks: Poland case study". Archives of Transport 36, n. 4 (31 dicembre 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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Levin, Ilya, Mark Korenblit e Vadim Talis. "STUDY OF SOCIAL NETWORKS’ DYNAMICS BY SIMULATION WITHIN THE NODEXL-EXCEL ENVIRONMENT". Problems of Education in the 21st Century 54, n. 1 (20 giugno 2013): 125–37. http://dx.doi.org/10.33225/pec/13.54.125.

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The present study is an analysis of the learning activity, which constitutes simulation of networks and studying their functioning and dynamics. The study is based on using network-like learning environments. Such environments allow building computer models of the network graphs. According to the suggested approach, the students construct dynamic computer models of the networks' graphs, thus implementing various algorithms of such networks’ dynamics. The suggested tool for building the models is the software environment comprising network analysis software NodeXL and a standard spreadsheet Excel. The proposed approach enables the students to visualize the network's dynamics. The paper presents specific examples of network models and various algorithms of the network's dynamics, which were developed based on the proposed approach. Key words: learning environments, modelling, social networks.

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Jia, Wenjie, e Tao Jiang. "Information-defined networks: A communication network approach for network studies". China Communications 18, n. 7 (luglio 2021): 197–210. http://dx.doi.org/10.23919/jcc.2021.07.016.

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Kwon, Minhae, e Hyunggon Park. "Network Coding Based Evolutionary Network Formation for Dynamic Wireless Networks". IEEE Transactions on Mobile Computing 18, n. 6 (1 giugno 2019): 1316–29. http://dx.doi.org/10.1109/tmc.2018.2861001.

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Yao, Hongyi, Sidharth Jaggi e Minghua Chen. "Passive Network Tomography for Erroneous Networks: A Network Coding Approach". IEEE Transactions on Information Theory 58, n. 9 (settembre 2012): 5922–40. http://dx.doi.org/10.1109/tit.2012.2204532.

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Kwon, Minhae, e Hyunggon Park. "Distributed Network Formation Strategy for Network Coding Based Wireless Networks". IEEE Signal Processing Letters 24, n. 4 (aprile 2017): 432–36. http://dx.doi.org/10.1109/lsp.2017.2670645.

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Koam, Ali N. A., Ali Ahmad e Yasir Ahmad. "Computation of reverse degree-based topological indices of hex-derived networks". AIMS Mathematics 6, n. 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 networks like logistical networks, gene regulatory networks, metabolic networks, social networks, derived networks. Topological index is a numerical number assigned to the molecular structure/netwrok which is used for correlation analysis in physical, theoretical and environmental chemistry. The hex-derived networks are created by hexagonal networks of dimension $ t $, these networks have an assortment of valuable applications in computer science, medical science and engineering. In this paper we discuss the reverse degree-based topological for third type of hex-derived networks.</p></abstract>

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Cai, Hongyun, Xiaomei Gong e Jianlei Han. "Analysis on the Spatial Structure and Interaction of Aviation Network and Tourism Efficiency Network in Major Cities in China". Academic Journal of Management and Social Sciences 2, n. 1 (27 marzo 2023): 134–45. http://dx.doi.org/10.54097/ajmss.v2i1.6504.

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Tourism efficiency is crucial for measuring sustainable tourism development. Examining the relationship between aviation and tourism efficiency networks is key to promoting their synergistic development in China's urban areas. This study employs various methods, such as complex network analysis method, entropy-weighted TOPSIS, tourism efficiency gravity model, and quadratic assignment procedure, to analyze the networks' spatial structure evolution characteristics and interaction effects. Results show that (1) China's major cities' aviation network has improved its organizational efficiency and formed a "double rhombus-single axis" spatial evolution pattern of the axis-spoke network. The number of intermediary networks and hub cities in the central and western regions has increased. (2) The tourism efficiency network adopts a "honeycomb" structure pattern with the simultaneous layout of "point-to-point" and "star" networks. The network's tourism efficiency follows "Pareto's Law," and tourism cities above the second level form a club group development. The tourism efficiency development potential area is shifting to the southwest. (3) The aviation and tourism efficiency networks exhibit a clear trend of synergistic evolution with a "path locking" phenomenon between them. Differences in tourism resource endowment, labor advantage, and capital advantage positively impact the aviation network's structure. Conversely, differences in revenue capacity and market scale negatively impact the structure. The aviation scale advantage, openness, intimacy, and influence exhibit decreasing positive effects on the tourism efficiency network's structure.

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Yiyuan Xie, Yiyuan Xie, e Zhu Yang Zhu Yang. "All-optical network interface from backbone networks to local area networks based on semiconductor optical amplifiers". Chinese Optics Letters 11, n. 11 (2013): 110605–8. http://dx.doi.org/10.3788/col201311.110605.

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Sharma, Anita, e Prashant Salwan. "Network Matters! Revisiting Social Networks". Academy of Management Proceedings 2017, n. 1 (agosto 2017): 17317. http://dx.doi.org/10.5465/ambpp.2017.17317abstract.

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

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Hampson, J. "Urban network development [distribution networks]". Power Engineering Journal 15, n. 5 (1 ottobre 2001): 224–32. http://dx.doi.org/10.1049/pe:20010504.

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Hirai, Shigeoki. "Multimedia Networks. Network and Telerobotics." Journal of the Robotics Society of Japan 15, n. 4 (1997): 516–19. http://dx.doi.org/10.7210/jrsj.15.516.

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Tizghadam, Ali, Weiwei Li e Alberto Leon-Garcia. "Network criticality in vehicular networks". ACM SIGMETRICS Performance Evaluation Review 40, n. 3 (4 dicembre 2012): 107–9. http://dx.doi.org/10.1145/2425248.2425278.

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Jiang, Chunxiao, Yan Chen, K. J. Ray Liu e Yong Ren. "Network economics in cognitive networks". IEEE Communications Magazine 53, n. 5 (maggio 2015): 75–81. http://dx.doi.org/10.1109/mcom.2015.7105644.

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Kilkenny, Maureen, e Nerys Fuller Love. "Network analysis and business networks". International Journal of Entrepreneurship and Small Business 21, n. 3 (2014): 303. http://dx.doi.org/10.1504/ijesb.2014.060894.

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Havlin, S., D. Y. Kenett, A. Bashan, J. Gao e H. E. Stanley. "Vulnerability of network of networks". European Physical Journal Special Topics 223, n. 11 (ottobre 2014): 2087–106. http://dx.doi.org/10.1140/epjst/e2014-02251-6.

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Rutherford, Ian. "Network Theory and Theoric Networks". Mediterranean Historical Review 22, n. 1 (giugno 2007): 23–37. http://dx.doi.org/10.1080/09518960701538523.

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

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Huang, Gang, e Mark Tausig. "Network range in personal networks". Social Networks 12, n. 3 (settembre 1990): 261–68. http://dx.doi.org/10.1016/0378-8733(90)90009-x.

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Stalker, James, John Lasley, George Frederick, Renee McPherson, Paul Campbell, Brenda Philips e Bob Pasken. "A Nationwide Network of Networks". Bulletin of the American Meteorological Society 94, n. 10 (1 ottobre 2013): 1602–6. http://dx.doi.org/10.1175/1520-0477-94.10.1602.

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

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Jadhav, Pranavati, e 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, n. 12-SPECIAL ISSUE (31 dicembre 2019): 366–71. http://dx.doi.org/10.5373/jardcs/v11sp12/20193232.

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Fu Jie Tey, Fu Jie Tey, Tin-Yu Wu Fu Jie Tey, Yueh Wu Tin-Yu Wu e 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, n. 2 (marzo 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 provide large coverage to achieve load balancing. By combing Generative Adversarial Network (GAN) with the ns-3 network simulator, this paper uses neural networks in TensorFlow to optimize load balancing of mobile networks, increase the data throughput and reduce the packet loss rate. In addition, to discuss the load balancing problem, we take the data produced by the ns-3 network simulator as the real data for GAN.</p> <p> </p>

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Navghare, Tukaram, Aniket Muley e Vinayak Jadhav. "Siamese Neural Networks for Kinship Prediction: A Deep Convolutional Neural Network Approach". Indian Journal Of Science And Technology 17, n. 4 (26 gennaio 2024): 352–58. http://dx.doi.org/10.17485/ijst/v17i4.3018.

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Ahmed, Aseel K., e Abbas Akram Khorsheed. "Open network structure and smart network to sharing cybersecurity within the 5G network". Indonesian Journal of Electrical Engineering and Computer Science 27, n. 1 (1 luglio 2022): 573. http://dx.doi.org/10.11591/ijeecs.v27.i1.pp573-582.

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The <span>next-generation communication system incorporates information technology (IT) and operations technology (OT) for generating, delivering, and collecting, and obtaining communication power. We plan to include a brief outline of internet of thing (IoT) communication and its context, along with security concerns that arise for IoT data on the network and some methods for detecting and avoiding cyber security threats. With the rise of the 5G networks, we introduce the smart network's emergent technology and its opportunities and more cybersecurity issues. Whereas, finding or responding to a power outage is an essential part of system security That is why we will discuss the innumerable advantages of 5G networks and we must also cover the inevitable problems that we will encounter in power delivery. The use of smart IoT communication technologies is becoming more common in the energy sector, particularly with the network (5G. The smart network and energy flow integration Real-time data on generation, electricity distribution, and energy consumption is measured using computers and cutting-edge technologies. This information aids utility companies in managing electricity supply and demand, as well as price. While enhanced communication and information technologies are unquestionably crucial to </span>the smart network.

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Alic, Kemal, Mihael Mohorcic e Ales Svigelj. "Network and Traffic Design Aspects in Network-Coding-Enabled Wireless Networks". International Journal of Computers Communications & Control 14, n. 3 (31 maggio 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 design aspects for network-coding-enabled wireless mesh networks and applications are discussed. We have shown that opportunistic network coding can improve the performance of different networks and supported applications in terms of throughput, delay and jitter, although the benefits are not significant in all the cases. Thus, the use of opportunistic network coding should be considered upfront during the wireless network design phase in order to obtain the greatest benefits.

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46

Walton, N. S. "Utility Optimization in Congested Queueing Networks". Journal of Applied Probability 48, n. 1 (marzo 2011): 68–89. http://dx.doi.org/10.1239/jap/1300198137.

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We consider a multiclass single-server queueing network as a model of a packet switching network. The rates packets are sent into this network are controlled by queues which act as congestion windows. By considering a sequence of congestion controls, we analyse a sequence of stationary queueing networks. In this asymptotic regime, the service capacity of the network remains constant and the sequence of congestion controllers act to exploit the network's capacity by increasing the number of packets within the network. We show that the stationary throughput of routes on this sequence of networks converges to an allocation that maximises aggregate utility subject to the network's capacity constraints. To perform this analysis, we require that our utility functions satisfy an exponential concavity condition. This family of utilities includes weighted α-fair utilities for α > 1.

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47

Walton, N. S. "Utility Optimization in Congested Queueing Networks". Journal of Applied Probability 48, n. 01 (marzo 2011): 68–89. http://dx.doi.org/10.1017/s0021900200007646.

Testo completo

Abstract (sommario):

We consider a multiclass single-server queueing network as a model of a packet switching network. The rates packets are sent into this network are controlled by queues which act as congestion windows. By considering a sequence of congestion controls, we analyse a sequence of stationary queueing networks. In this asymptotic regime, the service capacity of the network remains constant and the sequence of congestion controllers act to exploit the network's capacity by increasing the number of packets within the network. We show that the stationary throughput of routes on this sequence of networks converges to an allocation that maximises aggregate utility subject to the network's capacity constraints. To perform this analysis, we require that our utility functions satisfy an exponential concavity condition. This family of utilities includes weighted α-fair utilities for α > 1.

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48

Hrabcak, David, Lubomir Dobos, Jan Papaj e Lubos Ovsenik. "Multilayered Network Model for Mobile Network Infrastructure Disruption". Sensors 20, n. 19 (25 settembre 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 technologies of 5G networks and the phenomenon known as the Internet of Things (IoT) will empower the functionality of different types of networks and interconnects them into one complex network. The proposed concept is oriented on resilient, smart city applications such as public safety and health and it is able to provide critical communication when fixed network infrastructure is destroyed by deploying smart sensors and unmanned aerial vehicles. The provided simulations shows that the proposed multilayered network concept is able to perform better than traditional WSN network in term of delivery time, average number of hops and data rate speed, when disruption scenario occurs.

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49

Todd, Brody, Abiose Ibigbami e 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 opposed to having to solve many, which could take a significant amount of time. Also, because the network family looks at a protection scheme across a variety of average nodal connectivities, a clearer picture of the scheme’s performance is gained compared to just running the simulation on a single network or select few networks.

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50

Usui, Shohei, Fujio Toriumi, Masato Matsuo, Takatsugu Hirayama e Kenji Mase. "Greedy Network Growth Model of Social Network Service". Journal of Advanced Computational Intelligence and Intelligent Informatics 18, n. 4 (20 luglio 2014): 590–97. http://dx.doi.org/10.20965/jaciii.2014.p0590.

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Abstract (sommario):

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 enabled us to reproduce 30 networks built up by existing network models. We thus show that the proposed model represents the expressiveness of many existing network models.

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