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Academic literature on the topic 'Network'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 September 2024

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Journal articles on the topic "Network"

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PELLEGRINI, Lilla, Monica LEBA, and Alexandru IOVANOVICI. "CHARACTERIZATION OF URBAN TRANSPORTATION NETWORKS USING NETWORK MOTIFS." Acta Electrotechnica et Informatica 20, no. 4 (January 21, 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 (June 1, 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 (March 30, 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, and SVEN HEIDENREICH. "HOW NETWORK MANAGERS CONTRIBUTE TO INNOVATION NETWORK PERFORMANCE." International Journal of Innovation Management 16, no. 06 (November 30, 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, 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 (September 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, and Lubomir DOBOS. "THE CONCEPT OF MULTILAYERED NETWORK MODEL FOR 5G NETWORKS." Acta Electrotechnica et Informatica 19, no. 3 (December 4, 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 (March 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 (April 10, 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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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.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 68-70).
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 matroidal network associated with a representable matroid over a finite field. As a consequence, we obtain a correspondence between scalar-linearly solvable networks and representable matroids over finite fields in the framework of matroidal networks. We prove a theorem about the scalar-linear solvability of networks and field characteristics. We provide a method for generating scalar-linearly solvable networks that are potentially different from the networks that we already know are scalar-linearly solvable. In our capacity region approach, we define a multi-dimensional object, called the network capacity region, associated with networks that is analogous to the rate regions in information theory. For the network routing capacity region, we show that the region is a computable rational polytope and provide exact algorithms and approximation heuristics for computing the region. For the network linear coding capacity region, we construct a computable rational polytope, with respect to a given finite field, that inner bounds the linear coding capacity region and provide exact algorithms and approximation heuristics for computing the polytope. The exact algorithms and approximation heuristics we present are not polynomial time schemes and may depend on the output size.
by Anthony Eli Kim.
M.Eng.

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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 Point (CAP) is located at each site within the community network. This provides the hosts and servers at that site with access to services on the community network and the Internet, it is the site's router. The CAP provides a web based interface (CAPgui) which allows configuration of the device and viewing of simple monitoring statistics. The Access Concentrator (AC) is the default router for the CAPs and the gateway to the Internet. It provides authenticated and encrypted communication between the network sites. The AC performs several monitoring functions, both for the individual sites and for the upstream Internet connection. The AC provides a means for centrally managing and effectively allocating Internet bandwidth by using the web based interface (ACgui). Bandwidth use can be allocated per user, per host and per site. The system is maintainable, extendable and customisable for different network architectures. The system was deployed successfully to two community networks. The Centre of Excellence (CoE) testbed network is a peri-urban network deployment whereas the Siyakhula Living Lab (SLL) network is a rural deployment. The results gathered conclude that the project was successful as the deployed system is more robust and more manageable than the previous systems.

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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. The reason is that wireless networks are typically subject to resource constraints (e.g. bandwidth, power supply) and possess some unique characteristics (e.g. broadcast, node mobility) that traditional wired networks do not possess. This necessitates developing new techniques that take account of properties of wireless networks and are able to achieve a good balance between performance and security. In this three-part dissertation we investigate techniques for providing network camouflaging services in wireless networks. In the first part, we address a specific problem in a hierarchical multi-task sensor network, i.e. hiding the links between observable traffic patterns and user interests. To solve the problem, a temporally constant traffic pattern, called cover traffic pattern, is needed. We describe two traf- fic padding schemes that implement the cover traffic pattern and provide algorithms for achieving the optimal energy efficiencies with each scheme. In the second part, we explore the design of a MIX-net based anonymity system in mobile ad hoc networks. The objective is to hide the source-destination relationship with respect to each connection. We survey existing MIX route determination algorithms that do not account for dynamic network topology changes, which may result in high packet loss rate and large packet latency. We then introduce adaptive algorithms to overcome this problem. In the third part, we explore the notion of providing anonymity support at MAC layer in wireless networks, which employs the broadcast property of wireless transmission. We design an IEEE 802.11-compliant MAC protocol that provides receiver anonymity for unicast frames and offers better reliability than pure broadcast protocol.

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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 they pose is the interference of cell phone transmission with navigational system of aircraft and the cost of implementation. Present research work attempts to address the above problem by designing an IP based network which is capable of connecting travelers from aircraft to the rest of the world.
Thesis (M.S)-- Wichita State University, College of Engineering, Dept. of Electrical and Computer Engineering
"July 2007."

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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 order to recover from the problem. In the latter case, the system needs to continuously monitor the relevant performance parameters. In this thesis, we develop means for performing such monitoring in the context of a News-on-Demand application. We have designed and implemented a distributed QoS monitoring facility that can be used by distributed multimedia applications for QoS monitoring and QoS violation detection. The system consists of a measurement component and an administrative component. The measurement component is responsible for continuously measuring the end-to-end QoS parameters of connections between the server and client application, while the administrative component is responsible for the overall administration of the monitoring system, which includes raising QoS violation notifications if the quality of service is violated.

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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. San Francisco: Network Press, 1997.

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Dean, Tamara. Network+ guide to networks. Cambridge, MA: 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. Amsterdam, The Netherlands: Techne Press, 2008.

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

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

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

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

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Reichl, Peter, Burkhard Stiller, and Bruno Tuffin, eds. Network Economics for Next Generation Networks. Berlin, Heidelberg: 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. Chichester, England: Wiley, 2004.

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Bird, Drew. Network+. 2nd ed. Scottsdale, AZ: 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, 26–37. London: 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, 602–5. Berlin, Heidelberg: 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, 90–108. Abingdon, Oxon ; New York, NY : Routledge, 2018. | Series: Routledge studies in governance and public policy ; 31: 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, 1088. Boston, MA: 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, 1–16. Berlin, Heidelberg: 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, 187–204. Berkeley, CA: 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, 645–67. Hoboken, NJ, USA: 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, 91–104. Cham: 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, 107–20. Fifth edition. | Boca Raton : Taylor & Francis, CRC Press, 2018.: 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, Vincenzo Sciancalepore, Xi Li, Zarrar Yousaf, and Marco Liebsch. "Network Slicing for 5G Networks." In 5G Networks: Fundamental Requirements, Enabling Technologies, and Operations Management, 327–70. Hoboken, NJ, USA: 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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Hagberg, Aric A., Daniel A. Schult, and Pieter J. Swart. "Exploring Network Structure, Dynamics, and Function using NetworkX." In Python in Science Conference, 11–15. 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 properties and structure measures: shortest paths, betweenness centrality, clustering, and degree distribution and many more. NetworkX can read and write various graph formats for easy exchange with existing data, and provides generators for many classic graphs and popular graph models, such as the Erdos-Renyi, Small World, and Barabasi-Albert models. The ease-of-use and flexibility of the Python programming language together with connection to the SciPy tools make NetworkX a powerful tool for scientific computations. We discuss some of our recent work studying synchronization of coupled oscillators to demonstrate how NetworkX enables research in the field of computational networks.

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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. Washington, D.C.: 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 crossbar and a traditional MIN in terms of fan-out and fan-in versus number of stages. We show how switching networks with self-routing capability and arbitrarily low packet contention rates can be achieved by replicating the unipath network. We also compare the performance and cost of such networks with those of the GSN. Finally, we discuss the utility of these new packet-switched networks for photonic switching using smart pixels.

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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}. California: 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 privacy-preserving network alignment based on the event sequences. Our CPP consists of a coupled node embedding layer and a neural point process module. The coupled node embedding layer embeds one network's nodes and explicitly models the alignment matrix between the two networks. Accordingly, it parameterizes the node embeddings of the other network by the push-forward operation. Given the node embeddings, the neural point process module jointly captures the dynamics of the two networks' event sequences. We learn the CPP model in a maximum likelihood estimation framework with an inverse optimal transport (IOT) regularizer. Experiments show that our CPP is compatible with various point process backbones and is robust to the model misspecification issue, which achieves encouraging performance on network alignment. The code is available at https://github.com/Dixin-s-Lab/CNPP.

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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 types based on their intrinsic mechanical and topological properties. Such characterization would improve our ability to select microscale network topologies that match the mechanical properties we observe in healthy and diseased native tissues [2]. It would also improve our ability to discern the outcome of microstructural changes in tissues (such as from remodeling or injury) on their overall mechanics.

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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 of computational performance along with a comparison between the neural network approach and other conventional methods, used to represent the chemistry, are presented and the ability of neural networks for representing a non-linear chemical system is illustrated.

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Zhang, Yizhou, Guojie Song, Lun Du, Shuwen Yang, and Yilun Jin. "DANE: Domain Adaptive Network Embedding." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/606.

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Recent works reveal that network embedding techniques enable many machine learning models to handle diverse downstream tasks on graph structured data. However, as previous methods usually focus on learning embeddings for a single network, they can not learn representations transferable on multiple networks. Hence, it is important to design a network embedding algorithm that supports downstream model transferring on different networks, known as domain adaptation. In this paper, we propose a novel Domain Adaptive Network Embedding framework, which applies graph convolutional network to learn transferable embeddings. In DANE, nodes from multiple networks are encoded to vectors via a shared set of learnable parameters so that the vectors share an aligned embedding space. The distribution of embeddings on different networks are further aligned by adversarial learning regularization. In addition, DANE's advantage in learning transferable network embedding can be guaranteed theoretically. Extensive experiments reflect that the proposed framework outperforms other state-of-the-art network embedding baselines in cross-network domain adaptation tasks.

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Sun, Yiwei, Suhang Wang, Tsung-Yu Hsieh, Xianfeng Tang, and Vasant Honavar. "MEGAN: A Generative Adversarial Network for Multi-View Network Embedding." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/489.

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Data from many real-world applications can be naturally represented by multi-view networks where the different views encode different types of relationships (e.g., friendship, shared interests in music, etc.) between real-world individuals or entities. There is an urgent need for methods to obtain low-dimensional, information preserving and typically nonlinear embeddings of such multi-view networks. However, most of the work on multi-view learning focuses on data that lack a network structure, and most of the work on network embeddings has focused primarily on single-view networks. Against this background, we consider the multi-view network representation learning problem, i.e., the problem of constructing low-dimensional information preserving embeddings of multi-view networks. Specifically, we investigate a novel Generative Adversarial Network (GAN) framework for Multi-View Network Embedding, namely MEGAN, aimed at preserving the information from the individual network views, while accounting for connectivity across (and hence complementarity of and correlations between) different views. The results of our experiments on two real-world multi-view data sets show that the embeddings obtained using MEGAN outperform the state-of-the-art methods on node classification, link prediction and visualization tasks.

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Castillo-Lema, José, Augusto José Venâncio Neto, Flavio de Oliveira Silva, and Sergio Takeo Kofuji. "Network Function Virtualization in Content-Centric Networks." In X Workshop de Pesquisa Experimental da Internet do Futuro. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/wpeif.2019.7696.

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Network Functions Virtualization (NFV) offers an alternative way to design, deploy, and manage networking functions and services by leveraging virtualization technologies to consolidate network functions into general-purpose hardware platforms. On the past years extensive effort has been made to evolve and mature NFV tecnologies over IP networks. However, little or no attempts at all have been made to incorporate NFV into Information-Centric Networks (ICN). This work explores the use and implementation of virtual Network Funtions (VNFS)in Content-Centric Networks (CCN), and proposes the use of the Named Function Networking (NFN) paradigm as means to implement network functions and services in this kind of networks, distributing the network functions and services through the networks nodes and providing flexibility to dynamically place functions in the network as required and without the need of a central controller.

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Hadi, Mohammad F., and Victor H. Barocas. "Generating Random Fiber Network Topologies That Mimic Previously Characterized Networks." In ASME 2013 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/sbc2013-14615.

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Fibrous proteins, such as collagen and elastin, form the underlying structure of many soft tissues. These proteins form micrometer-scale networks of varying topology that play a role in governing the mechanics of tissues at larger length scales [1]. The relationship between a network’s topology and its mechanics, however, are poorly understood. This disconnect presents an important challenge in constructing realistic multiscale models of tissues informed by collagen network micrographs and subsequently reconstructed networks [2]. Accurate multiscale simulations may require thousands to millions of such unique networks. It is imperative that a method be developed to generate random networks that are functionally similar to ones derived experimentally. In the current study, we present a probabilistic method for generating de novo networks that mimic the mechanical properties of previously characterized networks. We chose Delaunay and Voronoi networks as model targets because they have been used successfully to model the mechanics of collagenous tissues [3] and since their topologies are well characterized. Understanding the role of topology in network mechanics is fundamental to building improved models of the mechanics of fibrous soft tissues — models that can aid in the rational design of engineered tissues or that can help assess the mechanical impact of damage or disease on native tissues.

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Zhang, Daokun, Jie Yin, Xingquan Zhu, and Chengqi Zhang. "User Profile Preserving Social Network Embedding." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/472.

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This paper addresses social network embedding, which aims to embed social network nodes, including user profile information, into a latent low-dimensional space. Most of the existing works on network embedding only consider network structure, but ignore user-generated content that could be potentially helpful in learning a better joint network representation. Different from rich node content in citation networks, user profile information in social networks is useful but noisy, sparse, and incomplete. To properly utilize this information, we propose a new algorithm called User Profile Preserving Social Network Embedding (UPP-SNE), which incorporates user profile with network structure to jointly learn a vector representation of a social network. The theme of UPP-SNE is to embed user profile information via a nonlinear mapping into a consistent subspace, where network structure is seamlessly encoded to jointly learn informative node representations. Extensive experiments on four real-world social networks show that compared to state-of-the-art baselines, our method learns better social network representations and achieves substantial performance gains in node classification and clustering tasks.

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Reports on the topic "Network"

1

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

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Gont, F., and T. Chown. Network Reconnaissance in IPv6 Networks. RFC Editor, March 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), July 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), July 2017. http://dx.doi.org/10.2172/1755855.

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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. Fort Belvoir, VA: Defense Technical Information Center, January 1995. http://dx.doi.org/10.21236/ada388752.

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Sutton, Jeffrey P. Reconfigurable Network of Networks for Multiscale Computing. Fort Belvoir, VA: Defense Technical Information Center, April 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, September 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, July 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. Fort Belvoir, VA: Defense Technical Information Center, September 2013. http://dx.doi.org/10.21236/ada590144.

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Sutton, Jeffrey. Reconfigurable Network of Networks for Multi-Scale Computing. Fort Belvoir, VA: Defense Technical Information Center, August 2002. http://dx.doi.org/10.21236/ada405332.

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