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Добірка наукової літератури з теми "Network analysis"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 12 жовтня 2024

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Статті в журналах з теми "Network analysis"

1

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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2

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 (December 31, 2019): 366–71. http://dx.doi.org/10.5373/jardcs/v11sp12/20193232.

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3

CVS, Rajesh, and Nadikoppula Pardhasaradhi. "Analysis of Artificial Neural-Network." International Journal of Trend in Scientific Research and Development Volume-2, Issue-6 (October 31, 2018): 418–28. http://dx.doi.org/10.31142/ijtsrd18482.

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4

KalaiSelvi, Dr B., and Aruna K. "Network Traffic Analysis Using Wireshark." International Journal of Research Publication and Reviews 4, no. 12 (December 18, 2023): 1960–65. http://dx.doi.org/10.55248/gengpi.4.1223.123506.

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5

Blair, Alan D., and Jordan B. Pollack. "Analysis of Dynamical Recognizers." Neural Computation 9, no. 5 (July 1, 1997): 1127–42. http://dx.doi.org/10.1162/neco.1997.9.5.1127.

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Pollack (1991) demonstrated that second-order recurrent neural networks can act as dynamical recognizers for formal languages when trained on positive and negative examples, and observed both phase transitions in learning and interacted function system-like fractal state sets. Follow on work focused mainly on the extraction and minimization of a finite state automaton (FSA) from the trained network. However, such networks are capable of inducing languages that are not regular and therefore not equivalent to any FSA. Indeed, it may be simpler for a small network to fit its training data by inducing such a nonregular language. But when is the network's language not regular? In this article, using a low-dimensional network capable of learning all the Tomita data sets, we present an empirical method for testing whether the language induced by the network is regular. We also provide a detailed "-machine analysis of trained networks for both regular and nonregular languages.

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6

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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7

Lobastova, M., A. Matyukhin, and A. Muthanna. "Analysis of Network Reliability of Network Synchronization." Telecom IT 8, no. 4 (December 23, 2020): 93–99. http://dx.doi.org/10.31854/2307-1303-2020-8-4-93-99.

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This article describes the challenges of modern communication networks reliability, analyses ITU-T recommendations and regulations governing the communication networks reliability in Russian Federation. The network clock network is an integral part of digital communication networks. Therefore, the issue of the synchronization network reliability should be given great attention. Research subject. In this article, we discussed the reliability of the clock synchronization network. Method. The main mathematical tools are graph theory and probability theory. To implement the proposed method for assessing the structural reliability of the synchronization network, the direct search method is used. Core results. The results allow us to conclude that the proposed method can be applied to assess the structural reliability of the clock synchronization network. Practical relevance. The solution proposed in this article can be used for a reasonable assessment of the network structural reliability indicators, which is necessary for making a decision on the choice of a route for transmitting a synchronization signal.

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8

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

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

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9

WEN, HAO, ZHENG-FU HAN, GUANG-CAN GUO, and PEI-LIN HONG. "QKD NETWORKS WITH PASSIVE OPTICAL ELEMENTS: ANALYSIS AND ASSESSMENT." International Journal of Quantum Information 07, no. 06 (September 2009): 1217–31. http://dx.doi.org/10.1142/s0219749909005730.

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Анотація:

Quantum Key Distribution (QKD) networks are the trends toward multiple users' unconditional secure communication. Based on several passive optical devices, such as beam splitter, optical switch or wavelength divided multiplexer, various types of fiber-based QKD networks have been proposed. However, it is still hard to accurately assess these networks. To find the optimal solution, a general assessment that would not involve detailed schemes is quite necessary. In this paper, we introduce an evaluation method and analyze optical-device-based QKD networks including two rational aspects: (i) network connectivity and network bandwidth which reflect the network's flexibility and performance in theory; (ii) network cost that brings pragmatic restriction on the network construction in practice. Applying this model, we compare five typical types of optical-device-based QKD networks. The explicit results demonstrate the above networks' characteristics and some valuable conclusions.

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10

Hafner-Burton, Emilie M., Miles Kahler, and Alexander H. Montgomery. "Network Analysis for International Relations." International Organization 63, no. 3 (July 2009): 559–92. http://dx.doi.org/10.1017/s0020818309090195.

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International relations research has regarded networks as a particular mode of organization, distinguished from markets or state hierarchies. In contrast, network analysis permits the investigation and measurement of network structures—emergent properties of persistent patterns of relations among agents that can define, enable, and constrain those agents. Network analysis offers both a toolkit for identifying and measuring the structural properties of networks and a set of theories, typically drawn from contexts outside international relations, that relate structures to outcomes. Network analysis challenges conventional views of power in international relations by defining network power in three different ways: access, brokerage, and exit options. Two issues are particularly important to international relations: the ability of actors to increase their power by enhancing and exploiting their network positions, and the fungibility of network power. The value of network analysis in international relations has been demonstrated in precise description of international networks, investigation of network effects on key international outcomes, testing of existing network theory in the context of international relations, and development of new sources of data. Partial or faulty incorporation of network analysis, however, risks trivial conclusions, unproven assertions, and measures without meaning. A three-part agenda is proposed for future application of network analysis to international relations: import the toolkit to deepen research on international networks; test existing network theories in the domain of international relations; and test international relations theories using the tools of network analysis.

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Дисертації з теми "Network analysis"

1

Oesterheld, Matthias. "Analysis of biomolecular networks using a generic network analysis suite." kostenfrei, 2008. http://mediatum2.ub.tum.de/doc/646475/646475.pdf.

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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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3

Lim, Kok Seng. "Analysis of network management protocols in optical networks." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Mar%5FLim%5FK.pdf.

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4

Silva, do Monte Lima Jennifer. "Performance analysis of network composition in ambient networks." Universidade Federal de Pernambuco, 2007. https://repositorio.ufpe.br/handle/123456789/2637.

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Made available in DSpace on 2014-06-12T15:59:50Z (GMT). No. of bitstreams: 2 arquivo5648_1.pdf: 4215564 bytes, checksum: a4f0b99c7dc76ce7283ee541003ccb8a (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2007
Conselho Nacional de Desenvolvimento Científico e Tecnológico
Atualmente, o compartilhamento de recursos e oferta de serviços entre redes são permitidos apenas através de intensa configuração manual e acordos prévios entre as redes envolvidas. Devido às diferentes tecnologias de acesso, à heterogeneidade dos dispositivos e dos serviços e a mobilidade dos usuários, o gerenciamento dos recursos se torna uma tarefa ainda mais complexa. As Redes de Ambiente surgem para permitir a cooperação instantânea e dinâmica de redes heterogêneas pertencentes a diferentes domínios administrativos e tecnológicos, através de um novo conceito chamado de Composição de Redes. A Composição permite a disponibilização de serviços e o compartilhamento de recursos entre redes, via Acordo de Composição. O desempenho da composição tem um fator crucial na viabilidade das Redes de Ambiente, devido à alta demanda por composição em uma interação de um usuário típico com a rede. Estas composições de redes mudam todo o cenário e trazem novas complicações para o processo tornando necessária a avaliação da estabilidade e da escalabilidade das mesmas. Diante da impossibilidade de testar tais conceitos de forma prática e real, optouse por fazê-lo através de simulação. Para atingir este objetivo foi especificado e implementado um simulador para Composições de Redes de Ambiente. Este simulador tem como objetivo principal avaliar o desempenho da composição mostrando que a mesma não representa um gargalo para a implantação das Redes de Ambiente

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5

Woodbury, Nathan Scott. "Network Reconstruction and Vulnerability Analysis of Financial Networks." BYU ScholarsArchive, 2017. https://scholarsarchive.byu.edu/etd/6370.

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Анотація:

Passive network reconstruction is the process of learning a structured (networked) representation of a dynamic system through the use of known information about the structure of the system as well as data collected by observing the inputs into a system along with the resultant outputs. This work demonstrates an improvement on an existing network reconstruction algorithm so that the algorithm is capable of consistently and perfectly reconstructing a network when system inputs and outputs are measured without error. This work then extends the improved network reconstruction algorithm so that it functions even in the presence of noise as well as the situation where inputs into the system are unknown. Furthermore, this work demonstrates the capability of the new extended algorithms by reconstructing financial networks from stock market data, and then performing an analysis to understand the vulnerabilities of the reconstructed network to destabilization through localized attacks. The creation of these improved and extended algorithms has opened many theoretical questions, paving the way for future research into network reconstruction.

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6

Junuthula, Ruthwik Reddy. "Modeling, Evaluation and Analysis of Dynamic Networks for Social Network Analysis." University of Toledo / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1544819215833249.

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7

Hassan, Aamir, and Fida Mohammad. "Network Security Analysis." Thesis, Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-4002.

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Анотація:

Security is the second step after that a successful network has been deployed. There are many types of attacks that could potentially harm the network and an administrator should carefully document and plan the weak areas, where the network could be compromised. Attackers use special tools and techniques to find out all the possible ways of defeating the network security. This thesis addresses all the possible tools and techniques that attackers use to compromise the network. The purpose for exploring these tools will help an administrator to find the security holes before an attacker can. All of these tools in this thesis are only for the forensic purpose. Securing routers and switches in the best possible way is another goal. We in this part try to identify important ways of securing these devices, along with their limitations, and then determine the best possible way. The solution will be checked with network vulnerable tools to get the results. It is important to note that most of the attention in network security is given to the router, but far less attention is given to securing a switch. This thesis will also address some more ways of securing a switch, if there is no router in the network.

The opponent for the thesis was Yan Wang and the presentation time was 60 minutes.

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8

Mei, Jonathan B. "Principal Network Analysis." Research Showcase @ CMU, 2018. http://repository.cmu.edu/dissertations/1175.

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Many applications collect a large number of time series, for example, temperature continuously monitored by weather stations across the US or neural activity recorded by an array of electrical probes. These data are often referred to as unstructured. A first task in their analytics is often to derive a low dimensional representation { a graph or discrete manifold { that describes the inter relations among the time series and their intrarelations across time. In general, the underlying graphs can be directed and weighted, possibly capturing the strengths of causal relations, not just the binary existence of reciprocal correlations. Furthermore, the processes generating the data may be non-linear and observed in the presence of unmodeled phenomena or unmeasured agents in a complex networked system. Finally, the networks describing the processes may themselves vary through time. In many scenarios, there may be good reasons to believe that the graphs are only able to vary as linear combinations of a set of \principal graphs" that are fundamental to the system. We would then be able to characterize each principal network individually to make sense of the ensemble and analyze the behaviors of the interacting entities. This thesis acts as a roadmap of computationally tractable approaches for learning graphs that provide structure to data. It culminates in a framework that addresses these challenges when estimating time-varying graphs from collections of time series. Analyses are carried out to justify the various models proposed along the way and to characterize their performance. Experiments are performed on synthetic and real datasets to highlight their effectiveness and to illustrate their limitations.

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9

Zhou, Lin. "Active network management and uncertainty analysis in distribution networks." Thesis, University of Bath, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.675697.

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Анотація:

In distribution networks, the traditional way to eliminate network stresses caused by increasing generation and demand is to reinforce the primary network assets. A cheaper alternative is active network management (ANM) which refers to real-time network control to resolve power flow, voltage, fault current and security issues. However, there are two limitations in ANM. First, previous ANM strategies investigated generation side and demand side management separately. The generation side management evaluates the value from ANM in terms of economic generation curtailment. It does not consider the potential benefits from integrating demand side response such as economically shifting flexible load over time. Second, enhancing generation side management with load shifting requires the prediction of network stress whose accuracy will decrease as the lead time increases. The uncertain prediction implies the potential failure of reaching expected operational benefits. However, there is very limited investigation into the trade-offs between operational benefit and its potential risk. In order to tackle the challenges, there are two aspects of research work in this thesis. 1) Enhanced ANM. It proposes the use of electric vehicles (EVs) as responsive demand to complement generation curtailment strategies in relieving network stress. This is achieved by shifting flexible EV charging demand over time to absorb excessive wind generation when they cannot be exported to the supply network. 2) Uncertainty management. It adopts Sharpe Ratio and Risk Adjust Return On Capital concepts from financial risk management to help the enhanced ANM make operational decisions when both operational benefit and its associated risk are considered. Copula theory is applied to further integrate correlations of forecasting errors between nodal power injections (caused by wind and load forecasting) into uncertainty management. The enhanced ANM can further improve network efficiency of the existing distribution networks to accommodate increasing renewable generation. The cost-benefit assessment informs distribution network operators of the trade-off between investment in ANM strategy and in the primary network assets, thus helping them to make cost-effective investment decisions. The uncertainty management allows the impact of risks that arise from network stress prediction on the expected operational benefits to be properly assessed, thus extending the traditional deterministic cost-benefit assessment to cost-benefit-risk assessment. Moreover, it is scalable to other systems in any size with low computational burden, which is the major contribution of this thesis.

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10

鄧沛權 and Pui-kuen Tang. "Business network: network marketing : analysis of network marketing using business network theories." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1997. http://hub.hku.hk/bib/B31268316.

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Книги з теми "Network analysis"

1

Brandes, Ulrik, and Thomas Erlebach, eds. Network Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/b106453.

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2

Combined Higher Education Software Team., Environmental Systems Research Institute, and Manchester Computing Centre, eds. Network analysis. Manchester: Manchester Computing Centre, 1993.

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3

Hodgson. Novell Netware protocol and network analysis. Manchester: University of Manchester, Department of Computer Science, 1996.

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4

Freeman, Linton. Social Network Analysis. 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications Ltd, 2008. http://dx.doi.org/10.4135/9781446263464.

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5

Knoke, David, and Song Yang. Social Network Analysis. 2455 Teller Road, Thousand Oaks California 91320 United States of America: SAGE Publications, Inc., 2008. http://dx.doi.org/10.4135/9781412985864.

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6

Xu, Kuai. Network Behavior Analysis. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8325-1.

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7

Holt, Alan. Network Performance Analysis. London: Springer London, 2008. http://dx.doi.org/10.1007/978-1-84628-823-4.

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8

Fellin, Tommaso, and Michael Halassa, eds. Neuronal Network Analysis. Totowa, NJ: Humana Press, 2012. http://dx.doi.org/10.1007/978-1-61779-633-3.

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9

Bonald, Thomas, and Mathieu Feuillet. Network Performance Analysis. Hoboken, NJ, USA: John Wiley & Sons, Inc, 2011. http://dx.doi.org/10.1002/9781118602911.

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10

Horvath, Steve. Weighted Network Analysis. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-8819-5.

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Частини книг з теми "Network analysis"

1

Cellerino, Alessandro, and Michele Sanguanini. "Network analysis." In Transcriptome Analysis, 99–119. Pisa: Scuola Normale Superiore, 2018. http://dx.doi.org/10.1007/978-88-7642-642-1_7.

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2

Brinkmeier, Michael, and Thomas Schank. "Network Statistics." In Network Analysis, 293–317. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-31955-9_11.

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3

Baur, Michael, and Marc Benkert. "Network Comparison." In Network Analysis, 318–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-31955-9_12.

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4

Baumann, Nadine, and Sebastian Stiller. "Network Models." In Network Analysis, 341–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-31955-9_13.

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Costantini, Giulio, and Marco Perugini. "Network Analysis." In The Wiley Handbook of Personality Assessment, 74–89. Chichester, UK: John Wiley & Sons, Ltd, 2016. http://dx.doi.org/10.1002/9781119173489.ch6.

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6

Rennels, Donald C., and Hobart M. Hudson. "Network Analysis." In Pipe Flow, 49–60. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118275276.ch5.

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7

Costantini, Giulio, and Marco Perugini. "Network Analysis." In Encyclopedia of Personality and Individual Differences, 3184–87. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-24612-3_1332.

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Vanhoucke, Mario. "Network Analysis." In Integrated Project Management Sourcebook, 11–27. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27373-0_3.

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Gallupe, Owen. "Network Analysis." In The Handbook of Measurement Issues in Criminology and Criminal Justice, 555–75. Hoboken, NJ: John Wiley & Sons, Inc, 2016. http://dx.doi.org/10.1002/9781118868799.ch25.

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Morrissey, Sean. "Network Analysis." In iOS Forensic Analysis for iPhone, iPad, and iPod touch, 323–42. Berkeley, CA: Apress, 2010. http://dx.doi.org/10.1007/978-1-4302-3343-5_10.

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Тези доповідей конференцій з теми "Network analysis"

1

Melo, Renato Silva, and André Luís Vignatti. "Preprocessing Rules for Target Set Selection in Complex Networks." In Brazilian Workshop on Social Network Analysis and Mining. Sociedade Brasileira de Computação, 2020. http://dx.doi.org/10.5753/brasnam.2020.11167.

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Анотація:

In the Target Set Selection (TSS) problem, we want to find the minimum set of individuals in a network to spread information across the entire network. This problem is NP-hard, so find good strategies to deal with it, even for a particular case, is something of interest. We introduce preprocessing rules that allow reducing the size of the input without losing the optimality of the solution when the input graph is a complex network. Such type of network has a set of topological properties that commonly occurs in graphs that model real systems. We present computational experiments with real-world complex networks and synthetic power law graphs. Our strategies do particularly well on graphs with power law degree distribution, such as several real-world complex networks. Such rules provide a notable reduction in the size of the problem and, consequently, gains in scalability.

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2

Bursztyn, Victor S., Marcelo Granja Nunes, and Daniel R. Figueiredo. "How Congressmen Connect: Analyzing Voting and Donation Networks in the Brazilian Congress." In Brazilian Workshop on Social Network Analysis and Mining. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/brasnam.2016.6451.

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The goal of this work is to analyze two of the most central activities in the life of a congressman: raising funds and voting bills. We investigate the Brazilian Congress to shed light on the relationships between the donations received by congressmen elected in 2014 and their voting behaviors during the year of 2015. We merged publicly available data obtained from the Brazilian House of Representatives and the Superior Electoral Court (TSE) in order to create a tripartite network containing campaign donors, elected congressmen, and legal bills. Using this data, we create two projected networks having congressmen as nodes and links given as follows: 1) congressmen who received donations from the same donors (donation network); and 2) congressmen who voted in accordance to each other on legal bills (voting network). After characterizing these networks, we propose three fundamental questions on the behavior of congressmen that could benefit from the methods and concepts provided by Network Science. Finally, we analyze the results and compare them to general domain knowledge.

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3

Maruyama, William Takahiro, and Luciano Antonio Digiampietri. "Co-authorship prediction in academic social network." In Brazilian Workshop on Social Network Analysis and Mining. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/brasnam.2016.6445.

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The prediction of relationships in a social network is a complex and extremely useful task to enhance or maximize collaborations by indicating the most promising partnerships. In academic social networks, prediction of relationships is typically used to try to identify potential partners in the development of a project and/or co-authors for publishing papers. This paper presents an approach to predict coauthorships combining artificial intelligence techniques with the state-of-the-art metrics for link predicting in social networks.

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4

Brandão, Michele A., Matheus A. Diniz, and Mirella M. Moro. "Using Topological Properties to Measure the Strength of Co-authorship Ties." In Brazilian Workshop on Social Network Analysis and Mining. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/brasnam.2016.6455.

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Studying the strength of ties in social networks allows to identify impact at micro-macro levels in the network, to analyze how distinct relationships play different roles, and so on. Indeed, the strength of ties has been investigated in many contexts with different goals. Here, we aim to address the problem of measuring ties strength in co-authorship social networks. Specifically, we present four case studies detailing problems with current metrics and propose a new one. Then, we build a co-authorship social network by using a real digital library and identify how the strength of ties relates to the quality of publication venues when measured by different topological properties. Our results show the best ranked venues have similar patterns of strength of co-authorship ties.

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5

Soares, Rafael Henrique Santos, Jorge H. C. Fernandes, and Ricardo Sampaio. "Formal Information Flows Among Top Authorities of the Brazilian Federal Government based on Co-word Analysis of Data Published in the Official Gazette." In Brazilian Workshop on Social Network Analysis and Mining. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/brasnam.2016.6450.

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This paper describes a methodology for modeling social networks of formal communications among top authorities of the Brazilian Federal Government grounded on data available in the government official gazette (Diário Oficial da União). The text of a large number of official publications such as presidential decrees, ministerial orders and authority nominations was analyzed for identification of citations to organizations and persons. The co-occurrence of names of persons in such publications created a network of relations among such persons. An ego-network was built around the president Dilma Rousseff. Metrics of social network analysis were collected and analyzed in an exploratory fashion.

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6

Oliveira, Davi Alves, Erica dos Santos Rodrigues, and Hernane Borges de Barros Pereira. "Affinity Networks as a Tool for Assessing Writing Processes: A Novel Method Utilizing Pause-Based Visibility Graphs." In Brazilian Workshop on Social Network Analysis and Mining. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/brasnam.2023.229991.

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This study proposes utilizing affinity networks to characterize writing profiles by examining pausing behavior during writing tasks. Data from 34 participants was collected using Inputlog. Pauses were used to create visibility graphs, and the resulting graph metrics were used to build an affinity network. The network was then analyzed to group writers based on their pausing behavior. Results indicate that this method can be used to identify different writing profiles of writers with distinct proficiency in text production. Future research should investigate if changes in pausing behavior can improved writing proficiency.

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7

Silva, Mariana O., Gabriel P. Oliveira, and Mirella M. Moro. "Analyzing Character Networks in Portuguese-language Literary Works." In Brazilian Workshop on Social Network Analysis and Mining. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/brasnam.2023.230585.

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Literary works are complex narratives with multifaceted character relationships. Studying these relationships can reveal important insights into the story’s structure and each character’s contribution to the plot development. This research investigates character networks in Portuguese-language literature using two main analytical approaches: structural network analysis and character importance metrics. Our analyses emphasize the significance of character networks in understanding the narrative structure of literary works and reveal the intricate interplay between characters in Portuguese-language literature. These findings deepen our comprehension of literary works’ fundamental structure and the characters’ pivotal role in shaping the story.

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8

Popescu, Daniela, Elena Serban, Carmen-Ema Panaite, and Abel Herna´ndez-Guerrero. "Hydraulic Analysis of a District Heating Network." In ASME 2008 9th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2008. http://dx.doi.org/10.1115/esda2008-59352.

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The aim of this work is to study the influence of pressure loss parameters from a district heating system on the distribution of fluid flow rates. The research was finalized by improving the mathematical model of a district heating network, which comprises an algebraic non-linear system that synthesizes flow balance equations of a stationary flow. This enhanced model indicates the influence of every consumer’s heat demand supplied from a district heating network on the fluid flow rates distribution based on the means of the implicit function theorem. The originality of the method consists of considering the network a sensitive system that responds to the variations of input parameters (pressure loss parameters) by variations of output parameter (fluid flow rates). The main advantage is that the engineer in charge with exploitation of the heating system may understand what happens with the flow allocations when pressure loss parameters of the network are different from the nominal ones, without making any measurements on the field or computations using new scenarios. The method presented in this paper facilitates the choice of the best decision concerning balancing and practical management of radial heating networks.

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9

Ashjaee, Mehdi, Reza Afzali, Mohammad Niknami, Mehdi Amiri, and Tooraj Yousefi. "Neural Network Analysis of Free Convection Around Isothermal Elliptic Tube." In ASME 8th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2006. http://dx.doi.org/10.1115/esda2006-95238.

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An artificial neural network (ANN) was applied successfully to predict laminar free convection heat transfer coefficient from an isothermal horizontal cylinder of elliptical cross section confined between two adiabatic walls. Neural networks were used since they constitute a general, powerful function-approximator tool proving able to represent a convectional heat transfer coefficient precisely in the present case. The input database for the network includes 171 experimental data points. The experiment is carried out using Mach-Zehnder Interferometry. Tube axis ratio, wall spacing to miner axis ratio of tube and Rayleigh number are variable parameters or the experimental study. The values of the average Nusselt numbers predicted by the network are in very good agreement with the available experimental data. Therefore the network is used to predict the unavailable data points within the range of our experimental results.

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10

Masys, Anthony. "Networks and network analysis for defence and security." In ASONAM '13: Advances in Social Networks Analysis and Mining 2013. New York, NY, USA: ACM, 2013. http://dx.doi.org/10.1145/2492517.2492602.

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Звіти організацій з теми "Network analysis"

1

Kalb, Jeffrey L., and David S. Lee. Network topology analysis. Office of Scientific and Technical Information (OSTI), January 2008. http://dx.doi.org/10.2172/1028919.

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2

Bonnett, Michaela, Chimdi Ezeigwe, Meaghan Kennedy, and Teri Garstka. Using Social Network Analysis to Link Community Health and Network Strength. Orange Sparkle Ball, July 2023. http://dx.doi.org/10.61152/scsf6662.

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Social network analysis (SNA) is a technique used to analyze social networks, whether it be composed of people, organizations, physical locations, or objects. It is being increasingly applied across a variety of sectors to gain insight into patterns of behavior and connectivity, the flow of information and behaviors, and to track and predict the effectiveness of interventions or programs. A key area associated with network strength using SNA is the health and wellness of individuals and communities. Both network strength and health and wellness are measured in many ways, which can obfuscate the association, so more consistency and further research is required. Despite this, the existing research using SNA to link characteristics of social networks to health and wellness find that stronger, more connected networks tend to be associated with better health outcomes. These results also present opportunities and insights for effective program implementation in response to disasters, to increase resilience, and to improve outcomes for individuals and communities.

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3

R.T. Rosche. IDCS NETWORK ALTERNATIVES ANALYSIS. Office of Scientific and Technical Information (OSTI), February 1995. http://dx.doi.org/10.2172/883443.

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4

Meyer, Robert A., and David A. Perreault. Communication Network Software Analysis. Fort Belvoir, VA: Defense Technical Information Center, June 1994. http://dx.doi.org/10.21236/ada281019.

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5

Zinilli, Antonio. From Basic to Advanced Network Analysis in R. Instats Inc., 2024. http://dx.doi.org/10.61700/t68209rotxkow930.

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This 2-day workshop offers a comprehensive understanding of network analysis and its application in R. Tailored for PhD students, professors, and professional researchers, this workshop provides a strong theoretical foundation and hands-on experience in visualizing and analyzing complex networks in socio-economic contexts, equipping participants with practical skills needed to apply network analysis in their own research.

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6

Lee, Hyun-Jung, HyunJu Shin, Kyu-Hye Lee, Seulah Lee, and Ye-Jin In. Semantic Network Analysis of Gorpcore. Ames (Iowa): Iowa State University. Library, January 2019. http://dx.doi.org/10.31274/itaa.8218.

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7

Christie, Alan M. Network Survivability Analysis Using Easel. Fort Belvoir, VA: Defense Technical Information Center, December 2002. http://dx.doi.org/10.21236/ada413664.

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8

Johannes, James D., Tim Lewis, Kyle Hoover, Andrew Fanning, and Chad Williams. Computer Network Analysis and Implementation. Fort Belvoir, VA: Defense Technical Information Center, October 1998. http://dx.doi.org/10.21236/ada391962.

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9

Powell, Bruce C. Artificial Neural Network Analysis System. Fort Belvoir, VA: Defense Technical Information Center, February 2001. http://dx.doi.org/10.21236/ada392390.

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10

Brunner, James, Aaron Robinson, and Chain Patrick Sam Guy. Is Transkingdom Network Analysis Possible? Office of Scientific and Technical Information (OSTI), November 2023. http://dx.doi.org/10.2172/2377943.

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