Academic literature on the topic 'Network measures'
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Journal articles on the topic "Network measures"
Kincaid, Rex K., and David J. Phillips. "Network topology measures." Wiley Interdisciplinary Reviews: Computational Statistics 3, no. 6 (June 14, 2011): 557–65. http://dx.doi.org/10.1002/wics.180.
Full textZhang, Wen Jie. "Network Security Vulnerabilities and Preventive Measures." Applied Mechanics and Materials 433-435 (October 2013): 1674–78. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.1674.
Full textAytaç, Aysun, and Tufan Turaci. "Vulnerability Measures of Transformation Graph Gxy+." International Journal of Foundations of Computer Science 26, no. 06 (September 2015): 667–75. http://dx.doi.org/10.1142/s0129054115500379.
Full textBoehmke, Frederick J., Olga Chyzh, and Cameron G. Thies. "Addressing Endogeneity in Actor-Specific Network Measures." Political Science Research and Methods 4, no. 1 (August 24, 2015): 123–49. http://dx.doi.org/10.1017/psrm.2015.34.
Full textKansky, Karl, and Pascal Danscoine. "Measures of network structure." Flux 5, no. 1 (1989): 89–121. http://dx.doi.org/10.3406/flux.1989.913.
Full textBanisch, Ralf, Péter Koltai, and Kathrin Padberg-Gehle. "Network measures of mixing." Chaos: An Interdisciplinary Journal of Nonlinear Science 29, no. 6 (June 2019): 063125. http://dx.doi.org/10.1063/1.5087632.
Full textLi, Yan. "Network Security Protection Measures." Advanced Materials Research 971-973 (June 2014): 1659–62. http://dx.doi.org/10.4028/www.scientific.net/amr.971-973.1659.
Full textVardi, Yehuda, and Cun-Hui Zhang. "Measures of Network Vulnerability." IEEE Signal Processing Letters 14, no. 5 (May 2007): 313–16. http://dx.doi.org/10.1109/lsp.2006.888290.
Full textBibi, Fizza, Hikmat Khan, Tassawar Iqbal, Muhammad Farooq, Irfan Mehmood, and Yunyoung Nam. "Ranking Authors in an Academic Network Using Social Network Measures." Applied Sciences 8, no. 10 (October 4, 2018): 1824. http://dx.doi.org/10.3390/app8101824.
Full textLordan, Oriol, and Jose M. Sallan. "Dynamic measures for transportation networks." PLOS ONE 15, no. 12 (December 3, 2020): e0242875. http://dx.doi.org/10.1371/journal.pone.0242875.
Full textDissertations / Theses on the topic "Network measures"
Traore, Abdoulaye S. "Mixed Network Interference Management with Multi-Distortion Measures." International Foundation for Telemetering, 2010. http://hdl.handle.net/10150/604294.
Full textThis paper presents a methodology for the management of interference and spectrum for iNET. It anticipates a need for heavily loaded test environments with Test Articles (TAs) operating over the horizon. In such cases, it is anticipated that fixed and ad hoc networks will be employed, and where spectrum reuse and interference will limit performance. The methodology presented here demonstrates how this can be accomplished in mixed networks.
Grando, Felipe. "Methods for the approximation of network centrality measures." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2018. http://hdl.handle.net/10183/186166.
Full textCentrality measures are an important analysis mechanism to uncover vital information about complex networks. However, these metrics have high computational costs that hinder their applications in large real-world networks. I propose and explain the use of artificial neural learning algorithms can render the application of such metrics in networks of arbitrary size. Moreover, I identified the best configuration and methodology for neural learning to optimize its accuracy, besides presenting an easy way to acquire and generate plentiful and meaningful training data via the use of a complex networks model that is adaptable for any application. In addition, I compared my prosed technique based on neural learning with different centrality approximation methods proposed in the literature, consisting of sampling and other artificial learning methodologies, and, I also tested the neural learning model in real case scenarios. I show in my results that the regression model generated by the neural network successfully approximates the metric values and is an effective alternative in real-world applications. The methodology and machine learning model that I propose use only a fraction of computing time with respect to other commonly applied approximation algorithms and is more robust than the other tested machine learning techniques.
Pellegrinet, Sarah <1988>. "Systemic Risk Measures and Connectedness: a network approach." Master's Degree Thesis, Università Ca' Foscari Venezia, 2015. http://hdl.handle.net/10579/6009.
Full textBenbrook, Jimmie Glen 1943. "A SYSTEM ANALYSIS OF A MULTILEVEL SECURE LOCAL AREA NETWORK (COMPUTER)." Thesis, The University of Arizona, 1986. http://hdl.handle.net/10150/275531.
Full textKim, Hyoungshick. "Complex network analysis for secure and robust communications." Thesis, University of Cambridge, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.610134.
Full textFu, Zehua. "Confidence measures in deep neural network based stereo matching." Thesis, Lyon, 2020. http://www.theses.fr/2020LYSEC014.
Full textDespite decades of enhancement since the first proposal of Barnard and Fischler’s, stereo matching approaches still suffer from imprecision, especially in the presence of occlusion, extreme lighting conditions and ambiguity. To overcome these inaccuracies, many methods, called confidence measures, have been proposed to assess the accuracy of the matching. In this thesis, we study state-of-the-art confidence measures and propose two measures, based on neural networks and deep learning, to improve the performance of stereo matching. A first proposed approach uses multi-modal data including the initial disparity and reference RGB images. The multi-modal architecture is subsequently improved by enlarging the Effective Receptive Field (ERF) enabling learning with more contextual information and thus leading to better detection of matching errors. Evaluated on KITTI2012 and KITTI2015 datasets, our multi-modal approach had achieved the best performance during the time. As a second approach, a Recurrent Neural Network (RNN) is proposed in order to refine the result of the stereo matching, step by step. The Gated Recurrent Units (GRU), combined with our multi-modal dilated convolutional network, use information from one step to guide refinement in the next. To the best of our knowledge, this is the first attempt to refine stereo matching based on an RNN. The proposed approach is easily applicable to different Convolutional Neural Networks (CNNs) in stereo matching to produce an effective and precise end-to-end solution. The experimental results prove significant improvements both on KITTI2012 and KITTI2015 datasets
Olsson, Eric J. "Literature survey on network concepts and measures to support research in network-centric operations." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03Jun%5FOlsson.pdf.
Full textFuloria, Shailendra. "Robust security for the electricity network." Thesis, University of Cambridge, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.610100.
Full textWoldearegay, Yonas, and Oumar Traore. "Optimization of Nodes in Mixed Network Using Three Distance Measures." International Foundation for Telemetering, 2011. http://hdl.handle.net/10150/595764.
Full textThis paper presents a method for the management of mixed networks as envisioned in future iNET applications and develops a scheme for global optimal performance for features that include signal to Noise Ratio (SNR), Quality of service (QoS), and Interference. This scheme demonstrates potential for significant enhancement of performance for dense traffic environments envisioned in future telemetry applications. Previous research conducted at Morgan State University has proposed a cellular and Ad hoc mixed network for optimum capacity and coverage using two distance measures: QoS and SNR. This paper adds another performance improvement technique, interference, as a third distance measure using an analytical approach and using extensive simulation with MATLAB. This paper also addresses solutions where performance parameters are correlated and uncorrelated. The simulations show the optimization of mixed network nodes using distance, traffic and interference measures all at one time. This has great potential in mobile communication and iNET.
Mooi, Roderick David. "A model for security incident response in the South African National Research and Education network." Thesis, Nelson Mandela Metropolitan University, 2014. http://hdl.handle.net/10948/d1017598.
Full textBooks on the topic "Network measures"
Hunt, Craig. Network Security. Sebastopol, CA: O’Reilly Media, 1998.
Find full textR, Simon Alan, ed. Network security. Boston: AP Professional, 1994.
Find full textAlbanese, Massimiliano (Computer scientist), author and Jajodia Sushil author, eds. Network hardening: An automated approach to improving network security. Cham: Springer, 2014.
Find full textJohn, Mallery, ed. Hardening network security. New York: McGraw-Hill/Osborne, 2005.
Find full textEric, Cole, ed. Network security fundamentals. Hoboken, N.J: Wiley, 2008.
Find full textNetwork security foundations. San Francisco, Calif: SYBEX, 2004.
Find full textNetwork defense. Clifton Park, NY: Course Technology, Cengage Learning, 2011.
Find full textRuss, Rogers, Criscuolo Paul, and Petruzzi Mike, eds. Nessus network auditing. 2nd ed. Burlington, MA: Syngress, 2008.
Find full textNetwork security auditing. Indianapolis, Ind: Cisco Press, 2010.
Find full textNetwork Perimeter Security. London: Taylor and Francis, 2003.
Find full textBook chapters on the topic "Network measures"
Horvath, Steve. "Association Measures and Statistical Significance Measures." In Weighted Network Analysis, 249–77. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-8819-5_10.
Full textZweig, Katharina A. "Classic Network Analytic Measures." In Lecture Notes in Social Networks, 91–108. Vienna: Springer Vienna, 2016. http://dx.doi.org/10.1007/978-3-7091-0741-6_4.
Full textMiyazaki, Syuji. "Gibbs measures for the network." In Emergent Intelligence of Networked Agents, 129–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-71075-2_10.
Full textAl-khateeb, Samer, and Nitin Agarwal. "Social Network Measures and Analysis." In SpringerBriefs in Cybersecurity, 27–44. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13690-1_2.
Full textHe, Baozhu, and Zhen He. "Centrality Measures in Telecommunication Network." In Advanced Research on Electronic Commerce, Web Application, and Communication, 337–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20370-1_55.
Full textZweig, Katharina A. "Understanding and Designing Network Measures." In Lecture Notes in Social Networks, 215–42. Vienna: Springer Vienna, 2016. http://dx.doi.org/10.1007/978-3-7091-0741-6_8.
Full textAleskerov, Fuad, Sergey Shvydun, and Natalia Meshcheryakova. "Centrality Indices in Network Analysis." In New Centrality Measures in Networks, 1–38. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003203421-1.
Full textMulekar, Madhuri S., and C. Scott Brown. "Distance and Similarity Measures." In Encyclopedia of Social Network Analysis and Mining, 1–16. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4614-7163-9_141-1.
Full textMulekar, Madhuri S., and C. Scott Brown. "Distance and Similarity Measures." In Encyclopedia of Social Network Analysis and Mining, 385–400. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-6170-8_141.
Full textMulekar, Madhuri S., and C. Scott Brown. "Distance and Similarity Measures." In Encyclopedia of Social Network Analysis and Mining, 647–62. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7131-2_141.
Full textConference papers on the topic "Network measures"
Singh, Gurpreet, Srinath Balaji, Jami J. Shah, David Corman, Ron Howard, Raju Mattikalli, and D. Stuart. "Evaluation of Network Measures as Complexity Metrics." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-70483.
Full textZheng, Xiaoxia. "Computer network security and measures." In Mechanical Engineering and Information Technology (EMEIT). IEEE, 2011. http://dx.doi.org/10.1109/emeit.2011.6023622.
Full textHou, Lvlin, Gang Liu, Songyang Lao, and Liang Bai. "Measures of network topology invulnerability." In 2012 2nd International Conference on Applied Robotics for the Power Industry (CARPI 2012). IEEE, 2012. http://dx.doi.org/10.1109/carpi.2012.6356504.
Full textRheinwalt, Aljoscha, Norbert Marwan, Jurgen Kurths, Peter Werner, and Friedrich-Wilhelm Gerstengarbe. "Boundary Effects in Network Measures of Spatially Embedded Networks." In 2012 SC Companion: High Performance Computing, Networking, Storage and Analysis (SCC). IEEE, 2012. http://dx.doi.org/10.1109/sc.companion.2012.72.
Full textScellato, Salvatore, Ilias Leontiadis, Cecilia Mascolo, Prithwish Basu, and Murtaza Zafer. "Understanding robustness of mobile networks through temporal network measures." In IEEE INFOCOM 2011 - IEEE Conference on Computer Communications. IEEE, 2011. http://dx.doi.org/10.1109/infcom.2011.5935006.
Full textCarneiro, Murillo G., Barbara C. Gama, and Otavio S. Ribeiro. "Complex Network Measures for Data Classification." In 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. http://dx.doi.org/10.1109/ijcnn52387.2021.9533608.
Full textHuang, Q., Y. Yuan, J. Goncalves, and M. A. Dahleh. "H2 norm based network volatility measures." In 2014 American Control Conference - ACC 2014. IEEE, 2014. http://dx.doi.org/10.1109/acc.2014.6859249.
Full textDehmer, Matthias, Stephan Borgert, and Frank Emmert-Streib. "Network Classes and Graph Complexity Measures." In 2008 First International Conference on Complexity and Intelligence of the Artificial and Natural Complex Systems. Medical Applications of the Complex Systems. Biomedical Computing (CANS). IEEE, 2008. http://dx.doi.org/10.1109/cans.2008.17.
Full textWinch, A. "Network performance improvement initiatives: a regional electricity company perspective." In IEE Colloquium on Measures to Prevent Power Blackouts. IEE, 1998. http://dx.doi.org/10.1049/ic:19980480.
Full textCarter, Martha A., and Mark E. Oxley. "Generalized measures of artificial neural network capabilities." In AeroSense '99, edited by Kevin L. Priddy, Paul E. Keller, David B. Fogel, and James C. Bezdek. SPIE, 1999. http://dx.doi.org/10.1117/12.342875.
Full textReports on the topic "Network measures"
Young, Stanley, and Dennis So Ting Fong. Arterial Network Performance Measures Software. Purdue University, December 2017. http://dx.doi.org/10.5703/1288284316570.
Full textFrantz, Terrill L., and Kathleen M. Carley. Relating Network Topology to the Robustness of Centrality Measures. Fort Belvoir, VA: Defense Technical Information Center, May 2005. http://dx.doi.org/10.21236/ada456108.
Full textYoung, Stanley, Christopher Day, and Dennis So Ting Fong. Network Performance Measures for Arterials—A Systematic Level Perspective. Purdue University, December 2017. http://dx.doi.org/10.5703/1288284316561.
Full textDuvvuri, Sarvani, and Srinivas S. Pulugurtha. Researching Relationships between Truck Travel Time Performance Measures and On-Network and Off-Network Characteristics. Mineta Transportation Institute, July 2021. http://dx.doi.org/10.31979/mti.2021.1946.
Full textBalza, Lenin H., Camilo De Los Rios, Alfredo Guerra, Luis Herrera-Prada, and Osmel Manzano. Unraveling the Network of the Extractive Industries. Inter-American Development Bank, April 2021. http://dx.doi.org/10.18235/0003191.
Full textMartínez-Ventura, Constanza, Jorge Ricardo Mariño-Martínez, and Javier Iván Miguélez-Márquez. Redundancy of Centrality Measures in Financial Market Infrastructures. Banco de la República de Colombia, August 2022. http://dx.doi.org/10.32468/be.1206.
Full textLausche, Barbara, Aaron Laur, and Mary Collins. Marine Connectivity Conservation Rules of Thumb for MPA and MPA Network Design. IUCN WCPA Connectivity Conservation Specialist Group’s Marine Connectivity Working Group, August 2021. http://dx.doi.org/10.53847/jxqa6585.
Full textBielinskyi, Andrii O., and Vladimir N. Soloviev. Complex network precursors of crashes and critical events in the cryptocurrency market. [б. в.], December 2018. http://dx.doi.org/10.31812/123456789/2881.
Full textHoaglund, Robert, and Walter Gazda. Assessment of Performance Measures for Security of the Maritime Transportation Network. Port Security Metrics: Proposed Measurement of Deterrence Capability. Fort Belvoir, VA: Defense Technical Information Center, January 2007. http://dx.doi.org/10.21236/ada471403.
Full textDuffy-Turner, M., I. M. Nettleton, M. G. Winter, and I. Webber. Forensic Examination of Critical Special Geotechnical Measures: Soil Nails Information Note. TRL, June 2022. http://dx.doi.org/10.58446/eprl1160.
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