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Relevant bibliographies by topics / Network

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Academic literature on the topic 'Network'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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Journal articles on the topic "Network"

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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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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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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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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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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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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Dissertations / Theses on the topic "Network"

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Ellerbrock, Thomas M. "Multilayer neural networks learnability, network generation, and network simplification /." [S.l. : s.n.], 1999. http://deposit.ddb.de/cgi-bin/dokserv?idn=958467897.

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Abrahamsen, Morten Hoie. "Sensemaking in networks Using network pictures to understand network change." Thesis, University of Manchester, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.506619.

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Kim, Anthony Eli. "On network coding capacity : matroidal networks and network capacity regions." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/62657.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 68-70).<br>One fundamental problem in the field of network coding is to determine the network coding capacity of networks under various network coding schemes. In this thesis, we address the problem with two approaches: matroidal networks and capacity regions. In our matroidal approach, we prove the converse of the theorem which states that, if a network is scalar-linearly solvable then it is a m

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Rivas, Antonio Canales. "Network management using active networks." Thesis, De Montfort University, 2006. http://hdl.handle.net/2086/4781.

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Wells, Daniel David. "Network management for community networks." Thesis, Rhodes University, 2010. http://hdl.handle.net/10962/d1006587.

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Community networks (in South Africa and Africa) are often serviced by limited bandwidth network backhauls. Relative to the basic needs of the community, this is an expensive ongoing concern. In many cases the Internet connection is shared among multiple sites. Community networks may also have a lack of technical personnel to maintain a network of this nature. Hence, there is a demand for a system which will monitor and manage bandwidth use, as well as network use. The proposed solution for community networks and the focus within this dissertation, is a system of two parts. A Community Access P

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Gaddam, Nishanth. "Network coding in wireless networks." [Ames, Iowa : Iowa State University], 2009. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1468982.

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Post, David L. "Network Management: Assessing Internet Network-Element Fault Status Using Neural Networks." Ohio : Ohio University, 2008. http://www.ohiolink.edu/etd/view.cgi?ohiou1220632155.

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Jiang, Shu. "Efficient network camouflaging in wireless networks." Texas A&M University, 2005. http://hdl.handle.net/1969.1/3067.

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Camouflaging is about making something invisible or less visible. Network camouflaging is about hiding certain traffic information (e.g. traffic pattern, traffic flow identity, etc.) from internal and external eavesdroppers such that important information cannot be deduced from it for malicious use. It is one of the most challenging security requirements to meet in computer networks. Existing camouflaging techniques such as traffic padding, MIX-net, etc., incur significant performance degradation when protected networks are wireless networks, such as sensor networks and mobile ad hoc networks.

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Bhatia, Anuj. "Voice network for aviation data networks." Thesis, Wichita State University, 2007. http://hdl.handle.net/10057/1500.

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Airline companies are always looking for various services they can offer to differentiate them from others and increase there market share. On the other hand passengers in the aircraft always want to be in touch with the rest of the world through phone or internet to make there time more productive. The drive of gaining market share by airline companies and advances in wireless and internet technology has made voice communication possible from the aircraft. Present technologies offer cell phone access or allow voice communication over internet through satellite link. The major disadvantage the

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Somalingam, Ramesh Ramvel. "Network performance monitoring for multimedia networks." Thesis, McGill University, 1996. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=23939.

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Multimedia applications require certain guarantees from the underlying system concerning quality of various parameters, such as the throughput, delay, loss rate etc. We assume that such quality of service (QoS) parameters are normally agreed upon for the duration of a session, and should be maintained throughout that session. If a QoS violation occurs, possibly due to the temporary overload of one of the system components, either the user will notice this and may request a renegotiation of the quality and cost parameters, or the system will automatically do some internal reconfiguration in ord

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Books on the topic "Network"

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W, Thomas Blair, ed. The Network Press QuickPath to NetWare 4.11 networks. 2nd ed. Network Press, 1997.

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Dean, Tamara. Network+ guide to networks. Course Technology, Thomson Learning, 2000.

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Jeroen, Van Schaick, Klaasen, I. T. (Ina T.), and Technische Universiteit Delft. Faculteit der Bouwkunde, eds. Urban networks: Network urbanism. Techne Press, 2008.

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Dean, Tamara. Enhanced Network+ guide to networks. Thomson/Course Technology, 2003.

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R, Coover Edwin, ed. Systems network architecture (SNA) networks. IEEE Computer Society Press, 1992.

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Coover, Edwin R. Systems network architecture (SNA) networks. IEEE Computer Society Press, 1992.

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1966-, Bijlsma Like, ed. Netwerk-steden-bouw =: Network urbanism. SUN, 2000.

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Reichl, Peter, Burkhard Stiller, and Bruno Tuffin, eds. Network Economics for Next Generation Networks. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01796-4.

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Abdelfatteh, Haidine, and Lehnert R, eds. Broadband powerline communications networks: Network design. Wiley, 2004.

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Bird, Drew. Network+. 2nd ed. Coriolis, 2002., 2002.

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Book chapters on the topic "Network"

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Assens, Christophe, and Aline Courie Lemeur. "From Network to “Network of Networks”." In Networks Governance, Partnership Management and Coalitions Federation. Palgrave Macmillan UK, 2016. http://dx.doi.org/10.1057/9781137566638_4.

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de Jesús Cruz Guzmán, José, and Zbigniew Oziewicz. "Network of Networks,." In Computational Science - ICCS 2004. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24687-9_82.

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Waring, Justin, Simon Bishop, and Bridget Roe. "Network contra network." In Decentring Health Policy. Routledge, 2017. http://dx.doi.org/10.4324/9781315310817-6.

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Weik, Martin H. "network-network interface." In Computer Science and Communications Dictionary. Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_12257.

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Pyka, Andreas, and Andrea Scharnhorst. "Introduction: Network Perspectives on Innovations: Innovative Networks – Network Innovation." In Innovation Networks. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-92267-4_1.

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Rao, Umesh Hodeghatta, and Umesha Nayak. "Understanding Networks and Network Security." In The InfoSec Handbook. Apress, 2014. http://dx.doi.org/10.1007/978-1-4302-6383-8_9.

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Fragouli, Christina. "Network Coding for Sensor Networks." In Handbook on Array Processing and Sensor Networks. John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470487068.ch20.

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Zaalouk, Adel, and Kostas Pentikousis. "Network Configuration in OpenFlow Networks." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16292-8_7.

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Comer, Douglas E. "Internet: A Network of Networks." In The Internet Book. Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9780429447358-10.

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Costa-Pérez, Xavier, Andrés Garcia-Saavedra, Fabio Giust, et al. "Network Slicing for 5G Networks." In 5G Networks: Fundamental Requirements, Enabling Technologies, and Operations Management. John Wiley & Sons, Inc., 2018. http://dx.doi.org/10.1002/9781119333142.ch9.

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Conference papers on the topic "Network"

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Sharma, Anurag. "AIOps: the Autonomous Network Journey." In Optical Fiber Communication Conference. Optica Publishing Group, 2025. https://doi.org/10.1364/ofc.2025.m3a.3.

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AI-powered Network 5.0 uses AI and deep learning to predict and manage network issues, optimize support, and streamline processes to enable fast responses to change, promising autonomous, efficient, and reliable networks. Full-text article not available; see video presentation

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Mores, Wannes, Satyajeet S. Bhonsale, Filip Logist, and Jan F. M. Van Impe. "Metabolic network reduction based on Extreme Pathway sets." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.132855.

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The use of metabolic networks is extremely valuable for design and optimisation of bioprocesses as they provide great insight into cellular metabolism. Within bioprocess optimisation, they have enabled better (economic) objective performance through more accurate network-based models. However, one of the drawbacks of using metabolic networks is their underdeterminacy, leading to non-unique flux distributions. Flux Balance Analysis (FBA) reduces this issue by making assumptions on the behaviour of the cell. However, for metabolic networks of higher complexity, can still struggle with underdeter

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Choi, Hyeongrak, Marc G. Davis, Álvaro G. Iñesta, and Dirk Englund. "Scalable Photonic Quantum Network." In CLEO: Fundamental Science. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/cleo_fs.2024.ftu3f.4.

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Jung, Young H., Subhash Talluri, and Imen Grida Ben Yahya. "A Path to Autonomous Network Operation for Future Mobile Network using GenAI and AI/ML." In Optical Fiber Communication Conference. Optica Publishing Group, 2025. https://doi.org/10.1364/ofc.2025.th3a.3.

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The evolution of mobile networks demands increasingly sophisticated approaches to network operation and resource management. While current architectures have improved through function decomposition and service-based designs, achieving true autonomy in network operations remains challenging. This paper presents a framework leveraging AI/ML and generative AI capabilities to enable the path toward autonomous network operations, demonstrating how cloud-native implementation enables intelligent scaling and self-optimization while reducing operational complexity.

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Hagberg, Aric A., Daniel A. Schult, and Pieter J. Swart. "Exploring Network Structure, Dynamics, and Function using NetworkX." In Python in Science Conference. SciPy, 2008. http://dx.doi.org/10.25080/tcwv9851.

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NetworkX is a Python language package for exploration and analysis of networks and network algorithms. The core package provides data structures for representing many types of networks, or graphs, including simple graphs, directed graphs, and graphs with parallel edges and self-loops. The nodes in NetworkX graphs can be any (hashable) Python object and edges can contain arbitrary data; this flexibility makes NetworkX ideal for representing networks found in many different scientific fields. In addition to the basic data structures many graph algorithms are implemented for calculating network p

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Krishnamoorthy, Ashok V., Fouad Kiamilev, and Sadik Esener. "A class of packet-switched extended generalized shuffle networks." In OSA Annual Meeting. Optica Publishing Group, 1992. http://dx.doi.org/10.1364/oam.1992.wv4.

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Extended generalized shuffle (EGS) networks1 have recently been proposed as a broad class of multistage interconnection networks (MINs) that include strictly non-blocking networks as well as networks with finite blocking probabilities. These networks can be viewed as extensions to generalized shuffle networks2 (GSNs) that include the facility to fan-out and fan-in at the input and output stages, respectively, of the interconnection network. We present a new class of packets-witched EGS networks. At the heart of these networks is a unipath EGS network that allows a continuous tradeoff between a

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Souza Neto, Natal Vieira de, Maurício Amaral Gonçalves, Daniel Ricardo Cunha Oliveira, Diego Nunes Molinos, Rodrigo Moreira, and Flávio de Oliveira Silva. "Evolved NWDAF Towards a Fully Distributed Artificial Intelligence in the 6G Network Architecture." In Workshop de Redes 6G. Sociedade Brasileira de Computação, 2024. http://dx.doi.org/10.5753/w6g.2024.3378.

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Artificial Intelligence (AI) is essential for evolving mobile networks towards 6G technology generation and beyond. In this context, the 3GPP has incorporated the Network Data Analytics Function (NWDAF) at the network’s core to leverage network data analytics, focusing on using analytics for automation. However, although NWDAF represents a significant advancement in this area, there is no consensus on deploying AI in the 6G network. This work suggests a framework for developing NWDAF that includes the necessary interfaces and behaviors to enhance the core network with AI capabilities Beyond 5G

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Luo, Dixin, Haoran Cheng, Qingbin Li, and Hongteng Xu. "Coupled Point Process-based Sequence Modeling for Privacy-preserving Network Alignment." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/678.

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Network alignment aims at finding the correspondence of nodes across different networks, which is significant for many applications, e.g., fraud detection and crime network tracing across platforms. In practice, however, accessing the topological information of different networks is often restricted and even forbidden, considering privacy and security issues. Instead, what we observed might be the event sequences of the networks' nodes in the continuous-time domain. In this study, we develop a coupled neural point process-based (CPP) sequence modeling strategy, which provides a solution to pri

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Christiansen, Eric M., Mohammad F. Hadi, and Victor H. Barocas. "Relating Network Topology to Network Mechanics." In ASME 2012 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/sbc2012-80684.

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Soft tissues are comprised of underlying fiber networks of collagen and other fibrous proteins and biopolymers. Thus, the ability to model the deformation of fiber networks is critical to understanding the mechanics of tissues in vivo and in vitro [1]. Complicating the issue, protein fiber networks are comprised of a range of different topologies that behave differently under load. There is a clear need for a method to derive network parameters that characterize the network and allow for the prediction of their behavior. In this study, we characterized several different random fiber network ty

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Mehdizadeh, Nasser S., Payam Sinaei, and Ali L. Nichkoohi. "Modeling Jones’ Reduced Chemical Mechanism of Methane Combustion With Artificial Neural Network." In ASME 2010 3rd Joint US-European Fluids Engineering Summer Meeting collocated with 8th International Conference on Nanochannels, Microchannels, and Minichannels. ASMEDC, 2010. http://dx.doi.org/10.1115/fedsm-icnmm2010-31186.

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The present work reports a way of using Artificial Neural Networks for modeling and integrating the governing chemical kinetics differential equations of Jones’ reduced chemical mechanism for methane combustion. The chemical mechanism is applicable to both diffusion and premixed laminar flames. A feed-forward multi-layer neural network is incorporated as neural network architecture. In order to find sets of input-output data, for adapting the neural network’s synaptic weights in the training phase, a thermochemical analysis is embedded to find the chemical species mole fractions. An analysis o

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Reports on the topic "Network"

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Sentz, Kari, and Jacob Green. Threat Network: Network of Networks Graphic. Office of Scientific and Technical Information (OSTI), 2014. http://dx.doi.org/10.2172/1162150.

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Gont, F., and T. Chown. Network Reconnaissance in IPv6 Networks. RFC Editor, 2016. http://dx.doi.org/10.17487/rfc7707.

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Kornell, James. Network of Networks Final Report 2016. Office of Scientific and Technical Information (OSTI), 2017. http://dx.doi.org/10.2172/1755855.

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Kornell, James. Network of Networks Final Report 2016. Office of Scientific and Technical Information (OSTI), 2017. http://dx.doi.org/10.2172/1755855.

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Snijders, Tom. Longitudinal Social Network Analysis. Instats Inc., 2024. https://doi.org/10.61700/zimra7qjrsnu11850.

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This three-day workshop provides an introductory overview of longitudinal social network analysis using the RSiena package, focusing on the application of Stochastic Actor-oriented Models to study network dynamics and the co-evolution of networks and behavior. Participants will gain practical skills in modeling network panel data, as well as modeling co-evolution of networks and behavior, for application of these techniques across various academic disciplines.

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Lin, Mengjou, Jenwei Hsieh, David H. Du, Joseph P. Thomas, and James A. MacDonald. Distributed Network Computing Over Local ATM Networks. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada388752.

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Sutton, Jeffrey P. Reconfigurable Network of Networks for Multiscale Computing. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada389675.

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Bocci, M., S. Boutros, and A. Sajassi. Applicability of Ethernet Virtual Private Network (EVPN) to Network Virtualization over Layer 3 (NVO3) Networks. Edited by J. Rabadan. RFC Editor, 2023. http://dx.doi.org/10.17487/rfc9469.

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Thubert, P., R. Wakikawa, and V. Devarapalli. Network Mobility Home Network Models. RFC Editor, 2007. http://dx.doi.org/10.17487/rfc4887.

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Zhang, Junshan. Networked Information Gathering in Stochastic Sensor Networks: Compressive Sensing, Adaptive Network Coding and Robustness. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada590144.

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