Academic literature on the topic 'Network data representation'
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Journal articles on the topic "Network data representation"
R.Tamilarasu and G. Soundarya Devi. "Improvising Connection In 5g By Means Of Particle Swarm Optimization Techniques." South Asian Journal of Engineering and Technology 14, no. 2 (April 30, 2024): 1–6. http://dx.doi.org/10.26524/sajet.2023.14.2.
Full textYe, Zhonglin, Haixing Zhao, Ke Zhang, Yu Zhu, and Zhaoyang Wang. "An Optimized Network Representation Learning Algorithm Using Multi-Relational Data." Mathematics 7, no. 5 (May 21, 2019): 460. http://dx.doi.org/10.3390/math7050460.
Full textArmenta, Marco, and Pierre-Marc Jodoin. "The Representation Theory of Neural Networks." Mathematics 9, no. 24 (December 13, 2021): 3216. http://dx.doi.org/10.3390/math9243216.
Full textAristizábal Q, Luz Angela, and Nicolás Toro G. "Multilayer Representation and Multiscale Analysis on Data Networks." International journal of Computer Networks & Communications 13, no. 3 (May 31, 2021): 41–55. http://dx.doi.org/10.5121/ijcnc.2021.13303.
Full textNguyễn, Tuấn, Nguyen Hai Hao, Dang Le Dinh Trang, Nguyen Van Tuan, and Cao Van Loi. "Robust anomaly detection methods for contamination network data." Journal of Military Science and Technology, no. 79 (May 19, 2022): 41–51. http://dx.doi.org/10.54939/1859-1043.j.mst.79.2022.41-51.
Full textDu, Xin, Yulong Pei, Wouter Duivesteijn, and Mykola Pechenizkiy. "Fairness in Network Representation by Latent Structural Heterogeneity in Observational Data." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 3809–16. http://dx.doi.org/10.1609/aaai.v34i04.5792.
Full textDongming Chen, Dongming Chen, Mingshuo Nie Dongming Chen, Jiarui Yan Mingshuo Nie, Jiangnan Meng Jiarui Yan, and Dongqi Wang Jiangnan Meng. "Network Representation Learning Algorithm Based on Community Folding." 網際網路技術學刊 23, no. 2 (March 2022): 415–23. http://dx.doi.org/10.53106/160792642022032302020.
Full textZhang, Xiaoxian, Jianpei Zhang, and Jing Yang. "Large-scale dynamic social data representation for structure feature learning." Journal of Intelligent & Fuzzy Systems 39, no. 4 (October 21, 2020): 5253–62. http://dx.doi.org/10.3233/jifs-189010.
Full textKapoor, Maya, Michael Napolitano, Jonathan Quance, Thomas Moyer, and Siddharth Krishnan. "Detecting VoIP Data Streams: Approaches Using Hidden Representation Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 13 (June 26, 2023): 15519–27. http://dx.doi.org/10.1609/aaai.v37i13.26840.
Full textGiannarakis, Nick, Alexandra Silva, and David Walker. "ProbNV: probabilistic verification of network control planes." Proceedings of the ACM on Programming Languages 5, ICFP (August 22, 2021): 1–30. http://dx.doi.org/10.1145/3473595.
Full textDissertations / Theses on the topic "Network data representation"
Lim, Chong-U. "Modeling player self-representation in multiplayer online games using social network data." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/82409.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (p. 101-105).
Game players express values related to self-expression through various means such as avatar customization, gameplay style, and interactions with other players. Multiplayer online games are now often integrated with social networks that provide social contexts in which player-to-player interactions take place, such as conversation and trading of virtual items. Building upon a theoretical framework based in machine learning and cognitive science, I present results from a novel approach to modeling and analyzing player values in terms of both preferences in avatar customization and patterns in social network use. To facilitate this work, I developed the Steam-Player- Preference Analyzer (Steam-PPA) system, which performs advanced data collection on publicly available social networking profile information. The primary contribution of this thesis is the AIR Toolkit Status Performance Classifier (AIR-SPC), which uses machine learning techniques including k-means clustering, natural language processing (NLP), and support vector machines (SVM) to perform inference on the data. As an initial case study, I use Steam-PPA to collect gameplay and avatar customization information from players in the popular, and commercially successful, multi-player first-person-shooter game Team Fortress 2 (TF2). Next, I use AIR-SPC to analyze the information from profiles on the social network Steam. The upshot is that I use social networking information to predict the likelihood of players customizing their profile in several ways associated with the monetary values of their avatars. In this manner I have developed a computational model of aspects of players' digital social identity capable of predicting specific values in terms of preferences exhibited within a virtual game-world.
by Chong-U Lim.
S.M.
Lee, John Boaz T. "Deep Learning on Graph-structured Data." Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-dissertations/570.
Full textAzorin, Raphael. "Traffic representations for network measurements." Electronic Thesis or Diss., Sorbonne université, 2024. http://www.theses.fr/2024SORUS141.
Full textMeasurements are essential to operate and manage computer networks, as they are critical to analyze performance and establish diagnosis. In particular, per-flow monitoring consists in computing metrics that characterize the individual data streams traversing the network. To develop relevant traffic representations, operators need to select suitable flow characteristics and carefully relate their cost of extraction with their expressiveness for the downstream tasks considered. In this thesis, we propose novel methodologies to extract appropriate traffic representations. In particular, we posit that Machine Learning can enhance measurement systems, thanks to its ability to learn patterns from data, in order to provide predictions of pertinent traffic characteristics.The first contribution of this thesis is a framework for sketch-based measurements systems to exploit the skewed nature of network traffic. Specifically, we propose a novel data structure representation that leverages sketches' under-utilization, reducing per-flow measurements memory footprint by storing only relevant counters. The second contribution is a Machine Learning-assisted monitoring system that integrates a lightweight traffic classifier. In particular, we segregate large and small flows in the data plane, before processing them separately with dedicated data structures for various use cases. The last contributions address the design of a unified Deep Learning measurement pipeline that extracts rich representations from traffic data for network analysis. We first draw from recent advances in sequence modeling to learn representations from both numerical and categorical traffic data. These representations serve as input to solve complex networking tasks such as clickstream identification and mobile terminal movement prediction in WLAN. Finally, we present an empirical study of task affinity to assess when two tasks would benefit from being learned together
SURANO, FRANCESCO VINCENZO. "Unveiling human interactions : approaches and techniques toward the discovery and representation of interactions in networks." Doctoral thesis, Politecnico di Torino, 2023. https://hdl.handle.net/11583/2975708.
Full textWoodbury, Nathan Scott. "Representation and Reconstruction of Linear, Time-Invariant Networks." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7402.
Full textMartignano, Anna. "Real-time Anomaly Detection on Financial Data." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281832.
Full textDetta arbete presenterar en undersökning av tillämpningar av Network Representation Learning (NRL) inom den finansiella industrin. Metoder inom NRL möjliggör datadriven kondensering av grafstrukturer till lågdimensionella och lätthanterliga vektorer.Dessa vektorer kan sedan användas i andra maskininlärningsuppgifter. Närmare bestämt, kan metoder inom NRL underlätta hantering av och informantionsutvinning ur beräkningsintensiva och storskaliga grafer inom den finansiella sektorn, till exempel avvikelsehantering bland finansiella transaktioner. Arbetet med data av denna typ försvåras av det faktum att transaktionsgrafer är dynamiska och i konstant förändring. Utöver detta kan noderna, dvs transaktionspunkterna, vara vitt skilda eller med andra ord härstamma från olika fördelningar.I detta arbete har Graph Convolutional Network (ConvGNN) ansetts till den mest lämpliga lösningen för nämnda tillämpningar riktade mot upptäckt av avvikelser i transaktioner. GraphSAGE har använts som utgångspunkt för experimenten i två olika varianter: en dynamisk version där vikterna uppdateras allteftersom nya transaktionssekvenser matas in, och en variant avsedd särskilt för bipartita (tvådelade) grafer. Dessa varianter har utvärderats genom användning av faktiska datamängder med avvikelsehantering som slutmål.
GARBARINO, DAVIDE. "Acknowledging the structured nature of real-world data with graphs embeddings and probabilistic inference methods." Doctoral thesis, Università degli studi di Genova, 2022. http://hdl.handle.net/11567/1092453.
Full textRANDAZZO, VINCENZO. "Novel neural approaches to data topology analysis and telemedicine." Doctoral thesis, Politecnico di Torino, 2020. http://hdl.handle.net/11583/2850610.
Full textLucke, Helmut. "On the representation of temporal data for connectionist word recognition." Thesis, University of Cambridge, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.239520.
Full textCori, Marcel. "Modèles pour la représentation et l'interrogation de données textuelles et de connaissances." Paris 7, 1987. http://www.theses.fr/1987PA077047.
Full textBooks on the topic "Network data representation"
service), SpringerLink (Online, ed. Guide to Computer Network Security. 2nd ed. London: Springer London, 2013.
Find full textHill, Richard. Guide to Cloud Computing: Principles and Practice. London: Springer London, 2013.
Find full textVarlamov, Oleg. Mivar databases and rules. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1508665.
Full textLaszlo, Berke, Murthy P. L. N, and United States. National Aeronautics and Space Administration., eds. Material data representation of hysteresis loops for Hastelloy X using artificial neural networks. [Washington, DC]: National Aeronautics and Space Administration, 1994.
Find full textLaszlo, Berke, Murthy P. L. N, and United States. National Aeronautics and Space Administration., eds. Material data representation of hysteresis loops for Hastelloy X using artificial neural networks. [Washington, DC]: National Aeronautics and Space Administration, 1994.
Find full textBrath, Richard Karl. Effective information visualization guidelines and metrics for 3D interactive representations of business data. [Toronto]: Brath, 1999.
Find full textS, Drew Mark, ed. Fundamentals of multimedia. Upper Saddle River, NJ: Pearson Prentice Hall, 2004.
Find full textRiaño, David. Knowledge Representation for Health-Care: ECAI 2010 Workshop KR4HC 2010, Lisbon, Portugal, August 17, 2010, Revised Selected Papers. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.
Find full textDiagrams 2010 (2010 Portland, Or.). Diagrammatic representation and inference: 6th international conference, Diagrams 2010, Portland, OR, USA, August 9-11, 2010 : proceedings. Berlin: Springer, 2010.
Find full textGerhard, Friedrich, Gottlob Georg, Katzenbeisser Stefan, Turán György, and SpringerLink (Online service), eds. SOFSEM 2012: Theory and Practice of Computer Science: 38th Conference on Current Trends in Theory and Practice of Computer Science, Špindlerův Mlýn, Czech Republic, January 21-27, 2012. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.
Find full textBook chapters on the topic "Network data representation"
Gaudel, Bijay, Donghai Guan, Weiwei Yuan, Deepanjal Shrestha, Bing Chen, and Yaofeng Tu. "Graph Representation Learning Using Attention Network." In Big Data, 137–47. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0705-9_10.
Full textSchestakov, Stefan, Paul Heinemeyer, and Elena Demidova. "Road Network Representation Learning with Vehicle Trajectories." In Advances in Knowledge Discovery and Data Mining, 57–69. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-33383-5_5.
Full textWang, Binglei, Tong Xu, Hao Wang, Yanmin Chen, Le Zhang, Lintao Fang, Guiquan Liu, and Enhong Chen. "Author Contributed Representation for Scholarly Network." In Web and Big Data, 558–73. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60259-8_41.
Full textZhang, Si, Yinglong Xia, Yan Zhu, and Hanghang Tong. "Representation Learning on Dynamic Network of Networks." In Proceedings of the 2023 SIAM International Conference on Data Mining (SDM), 298–306. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2023. http://dx.doi.org/10.1137/1.9781611977653.ch34.
Full textZhang, Yan, Zhao Zhang, Zheng Zhang, Mingbo Zhao, Li Zhang, Zhengjun Zha, and Meng Wang. "Deep Self-representative Concept Factorization Network for Representation Learning." In Proceedings of the 2020 SIAM International Conference on Data Mining, 361–69. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2020. http://dx.doi.org/10.1137/1.9781611976236.41.
Full textScheider, Simon, and Werner Kuhn. "Road Networks and Their Incomplete Representation by Network Data Models." In Geographic Information Science, 290–307. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-87473-7_19.
Full textZhang, Shaowei, Zhao Li, Xin Wang, Zirui Chen, and WenBin Guo. "TKGAT: Temporal Knowledge Graph Representation Learning Using Attention Network." In Advanced Data Mining and Applications, 46–61. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46664-9_4.
Full textSkabek, Krzysztof, and Łukasz Ząbik. "Network Transmission of 3D Mesh Data Using Progressive Representation." In Computer Networks, 325–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02671-3_38.
Full textChen, Weizheng, Jinpeng Wang, Zhuoxuan Jiang, Yan Zhang, and Xiaoming Li. "Hierarchical Mixed Neural Network for Joint Representation Learning of Social-Attribute Network." In Advances in Knowledge Discovery and Data Mining, 238–50. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57454-7_19.
Full textAnuradha, T., Arun Tigadi, M. Ravikumar, Paparao Nalajala, S. Hemavathi, and Manoranjan Dash. "Feature Extraction and Representation Learning via Deep Neural Network." In Computer Networks, Big Data and IoT, 551–64. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0898-9_44.
Full textConference papers on the topic "Network data representation"
Luo, Xuexiong, Jia Wu, Chuan Zhou, Xiankun Zhang, and Yuan Wang. "Deep Semantic Network Representation." In 2020 IEEE International Conference on Data Mining (ICDM). IEEE, 2020. http://dx.doi.org/10.1109/icdm50108.2020.00141.
Full textGao, Li, Hong Yang, Chuan Zhou, Jia Wu, Shirui Pan, and Yue Hu. "Active Discriminative Network Representation Learning." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/296.
Full textHansen, Brian, Leya Breanna Baltaxe-Admony, Sri Kurniawan, and Angus G. Forbes. "Exploring Sonic Parameter Mapping for Network Data Structures." In ICAD 2019: The 25th International Conference on Auditory Display. Newcastle upon Tyne, United Kingdom: Department of Computer and Information Sciences, Northumbria University, 2019. http://dx.doi.org/10.21785/icad2019.055.
Full textZhang, Xiangliang. "Mining Streaming and Temporal Data: from Representation to Knowledge." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/821.
Full textHou, Mingliang, Jing Ren, Falih Febrinanto, Ahsan Shehzad, and Feng Xia. "Cross Network Representation Matching with Outliers." In 2021 International Conference on Data Mining Workshops (ICDMW). IEEE, 2021. http://dx.doi.org/10.1109/icdmw53433.2021.00124.
Full textBandyopadhyay, Sambaran, Manasvi Aggarwal, and M. Narasimha Murty. "Self-supervised Hierarchical Graph Neural Network for Graph Representation." In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9377860.
Full textYu, Yanlei, Zhiwu Lu, Jiajun Liu, Guoping Zhao, and Ji-rong Wen. "RUM: Network Representation Learning Using Motifs." In 2019 IEEE 35th International Conference on Data Engineering (ICDE). IEEE, 2019. http://dx.doi.org/10.1109/icde.2019.00125.
Full textZhang, Chuxu, Meng Jiang, Xiangliang Zhang, Yanfang Ye, and Nitesh V. Chawla. "Multi-modal Network Representation Learning." In KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3394486.3406475.
Full textYang, Hong, Shirui Pan, Ling Chen, Chuan Zhou, and Peng Zhang. "Low-Bit Quantization for Attributed Network Representation Learning." 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.2019/562.
Full textGuan, Zhanming, Bin Wu, Bai Wang, and Hezi Liu. "Personality2vec: Network Representation Learning for Personality." In 2020 IEEE Fifth International Conference on Data Science in Cyberspace (DSC). IEEE, 2020. http://dx.doi.org/10.1109/dsc50466.2020.00013.
Full textReports on the topic "Network data representation"
Haynes, T., and D. Noveck, eds. Network File System (NFS) Version 4 External Data Representation Standard (XDR) Description. RFC Editor, March 2015. http://dx.doi.org/10.17487/rfc7531.
Full textShepler, S., M. Eisler, and D. Noveck, eds. Network File System (NFS) Version 4 Minor Version 1 External Data Representation Standard (XDR) Description. RFC Editor, January 2010. http://dx.doi.org/10.17487/rfc5662.
Full textHaynes, T. Network File System (NFS) Version 4 Minor Version 2 External Data Representation Standard (XDR) Description. RFC Editor, November 2016. http://dx.doi.org/10.17487/rfc7863.
Full textZanoni, Wladimir, Jimena Romero, Nicolás Chuquimarca, and Emmanuel Abuelafia. Dealing with Hard-to-Reach Populations in Panel Data: Respondent-Driven Survey (RDS) and Attrition. Inter-American Development Bank, October 2023. http://dx.doi.org/10.18235/0005194.
Full textHenderson, Tim, Mincent Santucci, Tim Connors, and Justin Tweet. National Park Service geologic type section inventory: Chihuahuan Desert Inventory & Monitoring Network. National Park Service, April 2021. http://dx.doi.org/10.36967/nrr-2285306.
Full textHenderson, Tim, Vincent Santucci, Tim Connors, and Justin Tweet. National Park Service geologic type section inventory: Northern Colorado Plateau Inventory & Monitoring Network. National Park Service, April 2021. http://dx.doi.org/10.36967/nrr-2285337.
Full textHenderson, Tim, Vincent Santucci, Tim Connors, and Justin Tweet. National Park Service geologic type section inventory: Klamath Inventory & Monitoring Network. National Park Service, July 2021. http://dx.doi.org/10.36967/nrr-2286915.
Full textHenderson, Tim, Vincent Santucci, Tim Connors, and Justin Tweet. National Park Service geologic type section inventory: Mojave Desert Inventory & Monitoring Network. National Park Service, December 2021. http://dx.doi.org/10.36967/nrr-2289952.
Full textHenderson, Tim, Vincet Santucci, Tim Connors, and Justin Tweet. National Park Service geologic type section inventory: North Coast and Cascades Inventory & Monitoring Network. National Park Service, March 2022. http://dx.doi.org/10.36967/nrr-2293013.
Full textHenderson, Tim, Vincent Santucci, Tim Connors, and Justin Tweet. National Park Service geologic type section inventory: Central Alaska Inventory & Monitoring Network. National Park Service, May 2022. http://dx.doi.org/10.36967/nrr-2293381.
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