Relevant bibliographies by topics / Network analysis and visualization

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Network analysis and visualization'

Author: Grafiati
Published: 25 January 2025

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Journal articles on the topic "Network analysis and visualization"

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Jeong, Dong Hyun, Bong-Keun Jeong, and Soo-Yeon Ji. "Multi-Resolution Analysis with Visualization to Determine Network Attack Patterns." Applied Sciences 13, no. 6 (March 16, 2023): 3792. http://dx.doi.org/10.3390/app13063792.

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Analyzing network traffic activities is imperative in network security to detect attack patterns. Due to the complex nature of network traffic event activities caused by continuously changing computing environments and software applications, identifying the patterns is one of the challenging research topics. This study focuses on analyzing the effectiveness of integrating Multi-Resolution Analysis (MRA) and visualization in identifying the attack patterns of network traffic activities. In detail, a Discrete Wavelet Transform (DWT) is utilized to extract features from network traffic data and investigate their capability of identifying attacks. For extracting features, various sliding windows and step sizes are tested. Then, visualizations are generated to help users conduct interactive visual analyses to identify abnormal network traffic events. To determine optimal solutions for generating visualizations, an extensive evaluation with multiple intrusion detection datasets has been performed. In addition, classification analysis with three different classification algorithms is managed to understand the effectiveness of using the MRA with visualization. From the study, we generated multiple visualizations associated with various window and step sizes to emphasize the effectiveness of the proposed approach in differentiating normal and attack events by forming distinctive clusters. We also found that utilizing MRA with visualization advances network intrusion detection by generating clearly separated visual clusters.
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Wang, Lidong. "Big Data and IT Network Data Visualization." International Journal of Mathematical, Engineering and Management Sciences 3, no. 1 (March 1, 2018): 9–16. http://dx.doi.org/10.33889/ijmems.2018.3.1-002.

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Visualization with graphs is popular in the data analysis of Information Technology (IT) networks or computer networks. An IT network is often modelled as a graph with hosts being nodes and traffic being flows on many edges. General visualization methods are introduced in this paper. Applications and technology progress of visualization in IT network analysis and big data in IT network visualization are presented. The challenges of visualization and Big Data analytics in IT network visualization are also discussed. Big Data analytics with High Performance Computing (HPC) techniques, especially Graphics Processing Units (GPUs) helps accelerate IT network analysis and visualization.
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Gelernter, Judith, and Kathleen M. Carley. "Spatiotemporal Network Analysis and Visualization." International Journal of Applied Geospatial Research 6, no. 2 (April 2015): 77–97. http://dx.doi.org/10.4018/ijagr.2015040105.

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Spatiotemporal social network analysis shows relationships among people at a particular time and location. This paper presents an algorithm that mines text for person and location words and creates connections among words. It shows how this algorithm output, when chunked by time intervals, may be visualized by third-party social network analysis software in the form of standard network pin diagrams or geographic maps. Its data sample comes from newspaper articles concerning the 2006 Darfur crisis in Sudan. Given an immense data sample, it would be possible to use the algorithm to detect trends that would predict the next geographic center(s) of influence and types of actors (foreign dignitaries or domestic leaders, for example). This algorithm should be widely generalizable to many text domains as long as the external resources are modified accordingly.
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Xu, Jennifer, and Hsinchun Chen. "Criminal network analysis and visualization." Communications of the ACM 48, no. 6 (June 2005): 100–107. http://dx.doi.org/10.1145/1064830.1064834.

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Murugesan, Sugeerth, Kristofer Bouchard, Jesse Brown, Mariam Kiran, Dan Lurie, Bernd Hamann, and Gunther H. Weber. "State-based network similarity visualization." Information Visualization 19, no. 2 (November 4, 2019): 96–113. http://dx.doi.org/10.1177/1473871619882019.

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We introduce an approach for the interactive visual analysis of weighted, dynamic networks. These networks arise in areas such as computational neuroscience, sociology, and biology. Network analysis remains challenging due to complex time-varying network behavior. For example, edges disappear/reappear, communities grow/vanish, or overall network topology changes. Our technique, TimeSum, detects the important topological changes in graph data to abstract the dynamic network and visualize one summary representation for each temporal phase, a state. We define a network state as a graph with similar topology over a specific time interval. To enable a holistic comparison of networks, we use a difference network to depict edge and community changes. We present case studies to demonstrate that our methods are effective and useful for extracting and exploring complex dynamic behavior of networks.
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Fitri, Marisa Meliana, Iswandi U, Nurhasan Syah, and Elsa Yuniarti. "Bibliometric Analysis of Spatial Stunting Using VOSviewer." Jurnal Penelitian Pendidikan IPA 9, no. 12 (December 20, 2023): 1298–305. http://dx.doi.org/10.29303/jppipa.v9i12.5914.

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Stunting, or stunted growth, is a serious global public health problem. The aim of this research is to investigate the dynamics of co-occurrence, trends and saturation of keywords in spatial studies related to stunting using the literature review method. This research uses bibliometric analysis with the help of Web Lens.org, Openrefine, and VOSviewer for data collection and analysis. VOSviewer can display bibliometric mapping in three different visualizations, including network visualization, overlay visualization, and density visualization. The findings of this research reveal seven topic clusters related to the subject of this research, with network visualization results showing that research with the keywords 'spatial' and 'stunting' is still relatively limited. The overlay visualization results show that this research trend only emerged at the beginning of 2020 and has not yet reached saturation point. In conclusion, there is still a lot of room for further research in this area, making spatial stunting an interesting and potentially useful area for further exploration.
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Papadopoulou, Olga, Themistoklis Makedas, Lazaros Apostolidis, Francesco Poldi, Symeon Papadopoulos, and Ioannis Kompatsiaris. "MeVer NetworkX: Network Analysis and Visualization for Tracing Disinformation." Future Internet 14, no. 5 (May 10, 2022): 147. http://dx.doi.org/10.3390/fi14050147.

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The proliferation of online news, especially during the “infodemic” that emerged along with the COVID-19 pandemic, has rapidly increased the risk of and, more importantly, the volume of online misinformation. Online Social Networks (OSNs), such as Facebook, Twitter, and YouTube, serve as fertile ground for disseminating misinformation, making the need for tools for analyzing the social web and gaining insights into communities that drive misinformation online vital. We introduce the MeVer NetworkX analysis and visualization tool, which helps users delve into social media conversations, helps users gain insights about how information propagates, and provides intuition about communities formed via interactions. The contributions of our tool lie in easy navigation through a multitude of features that provide helpful insights about the account behaviors and information propagation, provide the support of Twitter, Facebook, and Telegram graphs, and provide the modularity to integrate more platforms. The tool also provides features that highlight suspicious accounts in a graph that a user should investigate further. We collected four Twitter datasets related to COVID-19 disinformation to present the tool’s functionalities and evaluate its effectiveness.
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Selçuk, Mustafa, and Junior Hendri Wijaya. "Analysis of Agrarian Transformation Research." GOVERNABILITAS (Jurnal Ilmu Pemerintahan Semesta) 5, no. 1 (June 28, 2024): 68–79. http://dx.doi.org/10.47431/governabilitas.v5i1.426.

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Research on Agrarian Transformation is important because it touches on how a change or transformation occurs in the agricultural sector, which will also affect the condition of society. This research focuses on bibliometric analysis that seeks to analyze how research related to agrarian transformation is carried out. In this study, VOSviewer and Publish or Perish are used to facilitate data search and depiction. Data from the Scopus database is searched through Publish or Perish, with the keyword Agrarian Transformation. Furthermore, to visualize the data, VOSviewer version 1.6.19 is used to view network visualizations, overlays, and densities. The results showed that there were 7069 citations in all selected results, 706.90 citations per year, and 35.25 citations per paper. In addition, this study shows the 10 most influential authors in terms of the number of citations displayed. In network visualization, the networks that appear most often are land and production. The overlay visualization shows that before 2016 and after 2019, not much research or publication was done on the topic. Meanwhile, density visualization shows that the keywords that most often appear are agrarian transformation, transformation, agrarian change, development, land, and production, and agriculture. The limitation of this study is that the data is only taken through the Scopus database and the year of publication is taken only for ten years
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Benito Amat, Carlos. "Network analysis and visualization with Gephi." Redes. Revista hispana para el análisis de redes sociales 25, no. 1 (May 20, 2014): 201. http://dx.doi.org/10.5565/rev/redes.499.

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Lan, Wang Sen, Guo Hao Zhao, and Li Jun Hou. "Stocks Network Analysis Based on Visualization." Applied Mechanics and Materials 50-51 (February 2011): 323–27. http://dx.doi.org/10.4028/www.scientific.net/amm.50-51.323.

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In order to explore interactions among stocks, a weighted complex network was modeled, whose nodes were stocks, connection between nodes was established by their correlation, and the weight on corresponding edge was their correlation coefficient. The article analyzed some influence behaves among stocks by using visualization method, and gave some algorithms to find out network maximum spanning tree and core network. The result shows that the network is scale-free, nodes coupling are of homology, and some nodes are of strong influence on others.
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Dissertations / Theses on the topic "Network analysis and visualization"

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Köstinger, Harald. "ViNCent – Visualization of NetworkCentralities." Thesis, Linnéuniversitetet, Institutionen för datavetenskap, fysik och matematik, DFM, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-10793.

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In the area of information visualization social or biological networks are visualized ina way so that they can be explored easily and one can get more information about thestructure of the network out of it. The use of network centralities in the field of network analysis plays an importantrole when it comes to the rating of the relative importance of vertices within the networkstructure based on the neighborhood of them. Such a single network can be renderedeasily by the use of standard graph drawing algorithms. But it is not only the explorationof one centrality which is important. Furthermore, the comparison of two or more of themis important to get some further meaning out of it. When visualizing the comparisonof two or more network centralities we are facing new problems of how to visualizethem in a way to get out the most meaning of it. We want to be able to track all thechanges in the networks between two centralities as well as visualize the single networksas best as possible. In the life sciences centrality measures help scientists to understand theunderlying biological processes and have been successfully applied to different biologicalnetworks. The aim of the thesis is it to overcome those problems and to come up with a new solutionof how to visualize networks and its centralities. This thesis introduces a new way ofrendering networks including their centrality values along a circular view. Researches canthen be focused on the exploration of the centrality values including the network structure,without dealing with visual clutter or occlusions of nodes. Furthermore, filtering based instatistical data concerning the datasets and centrality values support this.
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Yu, En. "Social Network Analysis Applied to Ontology 3D Visualization." Miami University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=miami1206497854.

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Catanese, Salvatore Amato. "New perspectives in criminal network analysis: multilayer networks, time evolution, and visualization." Doctoral thesis, Università di Catania, 2017. http://hdl.handle.net/10761/3793.

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The work presented in this Dissertation reflects a long-term human, professional and cultural path started some years ago when I first developed LogAnalysis, a tool for the analysis and visualization of criminal and social networks. Since then, I devoted myself to the development of frameworks, algorithms and techniques for supporting intelligence and law enforcement agencies in the task of unveiling the CN structure hidden in communication data, identifying the target offenders for their removal or selecting effective strategies to disrupt a criminal organization. In a natural way, I successively focused on the evaluation of the resilience of criminal networks and on the multiplex formalism, which takes into account the various relationships existing within a criminal organization. In this context I introduce criminal network analysis tools: LogAnalysis, LogViewer, Semantic viewer and Failure simulator. I have been involved in the design, modeling, and writing of all of the works presented. In particular, I have also developed and tested all the visual tools included therein. Finally, I introduce Multiplex PBFS (Mx-PBFS) a novel multi-threaded parallel Breadth-First Search algorithm for categorical and inter-layer couplings multiplex networks, and the framework CriMuxnet (still under development) for multilayer criminal networks analysis based on high-quality 3D visualizations of network data. CriMuxnet was designed to work in conjunction with a 3D computer graphics (CG) packages: Autodesk Maya or Blender. CriMuxnet exploits 3D engine features to significantly improve both exploratory search and visualization strategy.
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Eiesland, Jon Wostryck. "Communities in a large social network : visualization and analysis." Thesis, Norwegian University of Science and Technology, Department of Physics, 2009. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-6409.

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Communities have been a hot topic in complex network research the last years. Several algorithms for detecting communities have been developed, and in this thesis we use the sequential clique percolation algorithm to detect communities in a large social network. Our network consists of 5.3 million mobile phone users, with mutual communication data aggregated over 18 weeks.

In this thesis we do a visual study of the communities, and we clearly see the nested community structure when we do clique percolation for dierent clique sizes. When we threshold the edge weights we see that the strongest edges are in the densest subcommunities and that the weakest edges keep the communities connected.

We also present numerical analysis of some selected structure and topology properties of the communities. Lastly we confirm, by numerical analysis of the available demographic data on the mobile phone users, that the communities are more conform with respect to zip code, age and sex compared to a reference network where the demographic attributes have been shuffled.


Samfunn har vært et hett emne innen forskning på komplekse nettverk de siste årene. Det har blitt utviklet flere algoritmer for å finne samfunn, og i denne oppgaven bruker vi sekvensiell klikkperkolasjon til å finne samfunn i et stort sosialt nettverk. Nettverket vårt består av 5.3 millioner mobiltelefonbrukere, med gjensidig kommunikasjonsdata aggregert over 18 uker.

I denne oppgaven gjør vi en visuell studie av samfunnene, og vi ser tydelig den vevde sammfunnsstrukturen når vi utfører klikkperkolasjon for ulike klikkstørrelser. Når vi setter terskler for lenkevektene ser vi at de sterkeste lenkene er i de tetteste undersamfunnene og at de svakeste lenkene holder samfunnene i kontakt med hverandre.

Vi presenterer også en numerisk analyse av noen utvalgte struktur- og topologiegenskaper hos samfunnene. Til slutt bekrefter vi, via numerisk analyse av den tilgjengelige demografiske informasjonen om mobiltelefonbrukerne, at samfunnene er mer konforme med tanke på postkode, alder og kjønn sammenlignet med et referansenettverk hvor de demografiske attributtene har blitt stokket om.

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El-Shehaly, Mai Hassan. "A Visualization Framework for SiLK Data exploration and Scan Detection." Thesis, Virginia Tech, 2009. http://hdl.handle.net/10919/34606.

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Network packet traces, despite having a lot of noise, contain priceless information, especially for investigating security incidents or troubleshooting performance problems. However, given the gigabytes of flow crossing a typical medium sized enterprise network every day, spotting malicious activity and analyzing trends in network behavior becomes a tedious task. Further, computational mechanisms for analyzing such data usually take substantial time to reach interesting patterns and often mislead the analyst into reaching false positives, benign traffic being identified as malicious, or false negatives, where malicious activity goes undetected. Therefore, the appropriate representation of network traffic data to the human user has been an issue of concern recently. Much of the focus, however, has been on visualizing TCP traffic alone while adapting visualization techniques for the data fields that are relevant to this protocol's traffic, rather than on the multivariate nature of network security data in general, and the fact that forensic analysis, in order to be fast and effective, has to take into consideration different parameters for each protocol. In this thesis, we bring together two powerful tools from different areas of application: SiLK (System for Internet-Level Knowledge), for command-based network trace analysis; and ComVis, a generic information visualization tool. We integrate the power of both tools by aiding simplified interaction between them, using a simple GUI, for the purpose of visualizing network traces, characterizing interesting patterns, and fingerprinting related activity. To obtain realistic results, we applied the visualizations on anonymized packet traces from Lawrence Berkley National Laboratory, captured on selected hours across three months. We used a sliding window approach in visually examining traces for two transport-layer protocols: ICMP and UDP. The main contribution of this research is a protocol-specific framework of visualization for ICMP and UDP data. We explored relevant header fields and the visualizations that worked best for each of the two protocols separately. The resulting views led us to a number of guidelines that can be vital in the creation of "smart books" describing best practices in using visualization and interaction techniques to maintain network security; while creating visual fingerprints which were found unique for individual types of scanning activity. Our visualizations use a multiple-views approach that incorporates the power of two-dimensional scatter plots, histograms, parallel coordinates, and dynamic queries.
Master of Science
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Kasemsri, Rawiroj Robert. "A Survey, Taxonomy, and Analysis of Network Security Visualization Techniques." Digital Archive @ GSU, 2006. http://digitalarchive.gsu.edu/cs_theses/17.

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Network security visualization is a relatively new field and is quickly gaining momentum. Network security visualization allows the display and projection of the network or system data, in hope to efficiently monitor and protect the system from any intrusions or possible attacks. Intrusions and attacks are constantly continuing to increase in number, size, and complexity. Textually reading through log files or other textual sources is currently insufficient to secure a network or system. Using graphical visualization, security information is presented visually, and not only by text. Without network security visualization, reading through log files or other textual sources is an endless and aggravating task for network security analysts. Visualization provides a method of displaying large volume of information in a relatively small space. It also makes patterns easier to detect, recognize, and analyze. This can help security experts to detect problems that may otherwise be missed in reading text based log files. Network security visualization has become an active research field in the past six years and a large number of visualization techniques have been proposed. A comprehensive analysis of the existing techniques is needed to help network security designers make informed decisions about the appropriate visualization techniques under various circumstances. Moreover, a taxonomy of the existing visualization techniques is needed to classify the existing network security visualization techniques and present a high level overview of the field. In this thesis, the author surveyed the field of network security visualization. Specifically, the author analyzed the network security visualization techniques from the perspective of data model, visual primitives, security analysis tasks, user interaction, and other design issues. Various statistics were generated from the literatures. Based on this analysis, the author has attempted to generate useful guidelines and principles for designing effective network security visualization techniques. The author also proposed a taxonomy for the security visualization techniques. To the author’s knowledge, this is the first attempt to generate a taxonomy for network security visualization. Finally, the author evaluated the existing network security visualization techniques and discussed their characteristics and limitations. For future research, the author also discussed some open research problems in this field. This research is a step towards a thorough analysis of the problem space and the solution space in network security visualization.
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Perer, Adam Nathaniel. "Integrating statistics and visualization to improve exploratory social network analysis." College Park, Md.: University of Maryland, 2008. http://hdl.handle.net/1903/8502.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2008.
Thesis research directed by: Dept. of Computer Science. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Freet, David Nathan. "A Security Visualization Analysis Methodology for Improving Network Intrusion Detection Efficiency." Thesis, Indiana State University, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10261868.

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The flood of raw data generated by intrusion detection and other network monitoring devices can be so overwhelming that it causes great difficulty in detecting patterns that might indicate malicious traffic. In order to more effectively monitor and process network and forensic data within a virtualized environment, Security Visualization (SecViz) provides software-based visual interfaces to analyze live and logged network data within the domains of network security, network and cloud forensics, attack prevention, compliance management, wireless security, secure coding, and penetration testing. Modern networks generate enormous amounts of data that is often stored in logs. Due to the lack of effective approaches to organizing and visualizing log data, most network monitoring tools focus at a high level on data throughput and efficiency, or dig too far down into the packet level to allow for useful analysis by network administrators. SecViz offers a simpler and more effective approach to analyzing the massive amounts of log data generated on a regular basis. Graphical representations make it possible to identify and detect malicious activity, and spot general trends and relationships among individual data points. The human brain can rapidly process visual information in a detailed and meaningful manner. By converting network security and forensic data into a human-readable picture, SecViz can address and solve complex data analysis challenges and significantly increase the efficiency by which data is processed by information security professionals.

This study utilizes the Snort intrusion detection system and SecViz tools to monitor and analyze various attack scenarios in a virtualized cloud computing environment. Real-time attacks are conducted in order to generate traffic and log data that can then be re-played in a number of software applications for analysis. A Java-based program is written to aggregate and display Snort data, and then incorporated into a custom Linux-based software environment along with select open-source SecViz tools. A methodology is developed to correlate Snort intrusion alerts with log data in order to create a visual picture that can significantly enhance the identification of malicious network activity and discrimination from normal traffic within a virtualized cloud-based network.

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Anantachai, Arnond. "A New Mobile Network Simulation And Analysis System And The Use Of Network Visualizations Through An End-User Graphics Package." OpenSIUC, 2010. https://opensiuc.lib.siu.edu/theses/243.

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Network simulations often output a log file, which must be parsed to understand the details of the simulation. Visualizations of these simulations are used to make debugging and analysis easier, and there are many visualizers that will display the simulation in 2D. Those in 3D do not fully utilize 3D graphics operations to visualize asimulation. This thesis explores the ways 3D graphics can be used to further enhance a visualization. To do this, it introduces a new network simulator and a visualizer, consisting of an analyzer, which collects statistics about a simulation, and a renderer, which leverages an existing program package for rendering.
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Dalton, Andrew R. "Analysis, instrumentation, and visualization of embedded network systems a testbed-based approach /." Connect to this title online, 2008. http://etd.lib.clemson.edu/documents/1219849076/.

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Books on the topic "Network analysis and visualization"

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De Domenico, Manlio. Multilayer Networks: Analysis and Visualization. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-75718-2.

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Zhao, Dangzhi, and Andreas Strotmann. Analysis and Visualization of Citation Networks. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-031-02291-3.

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Manavis, T. D. Visualization of graph networks for the analysis ofpiecewiselinear systems. Manchester: UMIST, 1994.

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Turdukulov, Ulanbek Duyshokovich. Visualizing the evolution of image features in time-series: Supporting the exploration of sensor data = Visualisatie van veranderende objecten in multitemporele beelden : ondersteuning van de exploratie van sensor gegevens. Utrecht: Universiteit Utrecht, 2007.

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Schich, Maximilian. Rezeption und Tradierung als komplexes Netzwerk: Der CENSUS und visuelle Dokumente zu den Thermen in Rom. München: Biering & Brinkmann, 2009.

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Kerren, Andreas, Helen C. Purchase, and Matthew O. Ward, eds. Multivariate Network Visualization. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06793-3.

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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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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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Bailey, David H., Naomi Simone Borwein, Richard P. Brent, Regina S. Burachik, Judy-anne Heather Osborn, Brailey Sims, and Qiji J. Zhu, eds. From Analysis to Visualization. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-36568-4.

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Lee, Eun Young, Johannes Novotny, and Michael Wagreich. Subsidence Analysis and Visualization. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-76424-5.

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Book chapters on the topic "Network analysis and visualization"

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Pretorius, Johannes, Helen C. Purchase, and John T. Stasko. "Tasks for Multivariate Network Analysis." In Multivariate Network Visualization, 77–95. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06793-3_5.

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Tan, Yuanru, Cesar Hinojosa, Cody Marquart, Andrew R. Ruis, and David Williamson Shaffer. "Epistemic Network Analysis Visualization." In Communications in Computer and Information Science, 129–43. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93859-8_9.

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Desai, Rishi M., William J. R. Longabaugh, and Wayne B. Hayes. "BioFabric Visualization of Network Alignments." In Recent Advances in Biological Network Analysis, 65–97. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57173-3_4.

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Zhao, Dangzhi, and Andreas Strotmann. "Disambiguation in Citation Network Analysis." In Analysis and Visualization of Citation Networks, 101–16. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-031-02291-3_4.

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Hu, Yifan. "Visualization of Large Networks." In Encyclopedia of Social Network Analysis and Mining, 1–9. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4614-7163-9_44-1.

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Hu, Yifan. "Visualization of Large Networks." In Encyclopedia of Social Network Analysis and Mining, 2328–36. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-6170-8_44.

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Hu, Yifan. "Visualization of Large Networks." In Encyclopedia of Social Network Analysis and Mining, 3315–23. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7131-2_44.

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Tripathi, Shailesh, Salissou Moutari, Matthias Dehmer, and Frank Emmert-Streib. "Visualization of Biological Networks Using NetBioV." In Computational Network Analysis with R, 307–33. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2016. http://dx.doi.org/10.1002/9783527694365.ch11.

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Zaidi, Faraz, Chris Muelder, and Arnaud Sallaberry. "Analysis and Visualization of Dynamic Networks." In Encyclopedia of Social Network Analysis and Mining, 1–12. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4614-7163-9_382-1.

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Zaidi, Faraz, Chris Muelder, and Arnaud Sallaberry. "Analysis and Visualization of Dynamic Networks." In Encyclopedia of Social Network Analysis and Mining, 37–48. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-6170-8_382.

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Conference papers on the topic "Network analysis and visualization"

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Baharudin, Shahidatul Arfah, Nurul Iman Muhammad Taufik, Sri Banu Munisamy, and Adidah Lajis. "Network Performance Analysis Of SCTP Protocol In Multihoming Transmission Through 5G Network." In 2024 International Visualization, Informatics and Technology Conference (IVIT), 207–11. IEEE, 2024. http://dx.doi.org/10.1109/ivit62102.2024.10692896.

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Choe, Kiroong, Eunhye Kim, Sangwon Park, and Jinwook Seo. "Fields, Bridges, and Foundations: How Researchers Browse Citation Network Visualizations." In 2024 IEEE Visualization and Visual Analytics (VIS), 146–50. IEEE, 2024. https://doi.org/10.1109/vis55277.2024.00037.

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Oggier, Frederique, Silivanxay Phetsouvanh, and Anwitaman Datta. "BiVA: Bitcoin Network Visualization & Analysis." In 2018 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2018. http://dx.doi.org/10.1109/icdmw.2018.00210.

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Kang, Seung-hoon, and Juho Kim. "Network Forensic Analysis Using Visualization Effect." In 2008 International Conference on Convergence and Hybrid Information Technology. IEEE, 2008. http://dx.doi.org/10.1109/ichit.2008.165.

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Gino, Henrique L. S., Diogenes S. Pedro, Jean R. Ponciano, Claudio D. G. Linhares, and Agma J. M. Traina. "Exploratory Analysis on Market Basket Data using Network Visualization." In Brazilian Workshop on Social Network Analysis and Mining. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/brasnam.2023.229505.

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Market basket analysis is a powerful technique for understanding customer behavior and optimizing business strategies based on that understanding. Market-based analysis over time using visualization techniques can provide insights into market trends and relations, simplify complex data, and communicate insights effectively, which can help organizations make more informed decisions. This paper leverages a dataset focused on the users’ incomes and temporal aspects of market purchases. We modeled this dataset as three distinct temporal networks and performed an exploratory evaluation identifying patterns and anomalies in the data. More specifically, we identified groups of related products, indicating thematic purchases, and evaluated the impact of demographic factors, such as income, on customer spending.
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Wei, Yingmei, and Xiaolei Du. "Two-Layer Network Visualization for Comprehensive Analysis." In 2017 IEEE Second International Conference on Data Science in Cyberspace (DSC). IEEE, 2017. http://dx.doi.org/10.1109/dsc.2017.99.

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Kim, Hoin, Inyong Lee, Jaeik Cho, and Jongsub Moon. "Visualization of network components for attack analysis." In 2009 IEEE Symposium on Computational Intelligence in Cyber Security (CICS). IEEE, 2009. http://dx.doi.org/10.1109/cicybs.2009.4925082.

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Kucher, Kostiantyn, Masoud Fatemi, and Mikko Laitinen. "Towards Visual Sociolinguistic Network Analysis." In 12th International Conference on Information Visualization Theory and Applications. SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010328202480255.

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Wu, Tongshuang, Yuan Yao, Yuqing Duan, Xinzhi Fan, and Huamin Qu. "NetworkSeer: Visual analysis for social network in MOOCs." In 2016 IEEE Pacific Visualization Symposium (PacificVis). IEEE, 2016. http://dx.doi.org/10.1109/pacificvis.2016.7465269.

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Zhao, Ying, Ralucca Gera, Quinn Halpin, and Jesse Zhou. "Visualization Techniques for Network Analysis and Link Analysis Algorithms." In 11th International Conference on Knowledge Discovery and Information Retrieval. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0008377805610568.

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Reports on the topic "Network analysis and visualization"

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Bethel, E. Wes. Query-Driven Network Flow Data Analysis and Visualization. Office of Scientific and Technical Information (OSTI), June 2006. http://dx.doi.org/10.2172/888963.

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Kahn, Russell L. Attack Analyzer: A Network Analysis and Visualization Tool. Fort Belvoir, VA: Defense Technical Information Center, February 2007. http://dx.doi.org/10.21236/ada464353.

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Bethel, E. Wes, Scott Campbell, Eli Dart, Jason Lee, Steven A. Smith, Kurt Stockinger, Brian Tierney, and Kesheng Wu. Interactive Analysis of Large Network Data Collections UsingQuery-Driven Visualization. Office of Scientific and Technical Information (OSTI), December 2005. http://dx.doi.org/10.2172/891627.

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Xu, Ran. Introduction to Social Network Analysis (Free Seminar). Instats Inc., 2024. https://doi.org/10.61700/zud3mrhp34b2l1910.

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This seminar provides an introduction to social network analysis (SNA), equipping researchers in social sciences, population health science, and systems science with essential tools to examine relationships and behaviors in varied contexts, including mHealth and social media. Attendees will gain a practical understanding of the basics of network theory, data analysis, visualization methods, and real-world applications of SNA.
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Etoty, Renee E., Robert F. Erbacher, and Christopher Garneau. Evaluation of the Presentation of Network Data via Visualization Tools for Network Analysts. Fort Belvoir, VA: Defense Technical Information Center, March 2014. http://dx.doi.org/10.21236/ada601647.

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Lu, Dengwei, Enjie Tang, Supeng Yin, Yizeng Sun, Yuquan Yuan, Tingjie Yin, Zeyu Yang, and Fan Zhang. Intraoperative strategies in identification and functional protection of parathyroid gland for patients with thyroidectomy: A network meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, November 2022. http://dx.doi.org/10.37766/inplasy2022.11.0109.

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Review question / Objective: To assess the roles of intraoperative visualization of parathyroid glands (IVPG) strategies including autofluorescence (AF), indocyanine green (ICG) fluorescence and carbon nanoparticles (CN) in identification and functional protection of parathyroid glands (PGs). Condition being studied: The IVPG strategy is essential for intraoperative identification and functional protection of PGs in patients undergoing thyroid surgery. Recent studies have revealed that autofluorescence (AF), indocyanine green (ICG) fluorescence, and carbon nanoparticles (CN) contribute to reducing the incidence of postoperative hypocalcemia by improving PGs identification(6-8). However, different IVPGs have respective superiority and inferiority in clinical application. Thus, this network meta-analysis aimed to systematically analyze the significance of IVPG strategy in identifying PGs and protecting their function.
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Rehrer, Sarah, Andrew Griffin, and Matthew Renner. Cross country mobility (CCM) modeling using triangulated irregular networks (TIN). Engineer Research and Development Center (U.S.), November 2022. http://dx.doi.org/10.21079/11681/46082.

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Cross country mobility (CCM) models terrain that has insufficient or unavailable infrastructure for crossing. This historically has been done with either hand-drawn and estimated maps or with raster-based terrain analysis, both of which have their own strengths and weaknesses. In this report the authors explore the possibility of using triangulated irregular networks (TINs) as a means of representing terrain characteristics used in CCM and discuss the possibilities of using such networks for routing capabilities in lieu of a traditional road-based network. The factors used to calculate CCM are modified from previous methods to capture a more accurate measurement of terrain characteristics. Using a TIN to store and represent CCM information achieves comparable results to raster cost analysis with the additional benefits of an integrated network useful for visualization and routing and a reduction in the number of related files. Additionally, TINs can in some cases more accurately show the contours of the landscape and reveal feature details or impediments that may be lost within a raster, thus improving the quality of CCM overlays.
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Kennedy, Meaghan, Michaela Bonnett, and Teri Garstka. A Model for Technology-Enabled Community Resilience. Orange Sparkle Ball, June 2024. http://dx.doi.org/10.61152/plcr9111.

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Introduction Tech-Enabled Community Resilience is an innovative model designed to enhance resilience and optimize impact in complex systems such as communities and ecosystems. The model leverages social network analysis and technology to visualize network dynamics, measure interactions, and implement targeted interventions. Model Structure The approach consists of two key stages: a Startup Phase focused on assembling champions and co-creating a shared vision, and a Steady-state Phase involving iterative measurement and intervention. By utilizing technology platforms for data collection and visualization, the model provides near real-time understanding of network functioning. Advantages Over Traditional Approaches Traditional resource mapping approaches provide a limited understanding of the network based on a static understanding of resources and a lack of complexity about network function. The Tech-Enabled Community Resilience model provides for a more dynamic, systems-thinking perspective. The model allows for precision interventions based on network structure, potentially influencing community-level outcomes. Case Studies and Research Findings Case studies from social care networks and economic development initiatives demonstrate the model's applicability across various contexts. Research findings linking network cohesion to improved community outcomes during crises, and network structure to increased innovation in ecosystems, underscore the model's potential impact. Future Directions Further model refinement includes the development of a portfolio of network-based interventions, integration of real-time data sources, and strategies for adaptive governance structures. This model represents a significant advancement in how to understand and harness complex systems for community resilience and impact optimization.
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Balyk, Nadiia, Yaroslav Vasylenko, Vasyl Oleksiuk, and Galina Shmyger. Designing of Virtual Cloud Labs for the Learning Cisco CyberSecurity Operations Course. [б. в.], June 2019. http://dx.doi.org/10.31812/123456789/3177.

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The article is devoted to the study of the problem of the cybersecurity basics teaching. The training of the ICT-specialties students using the course “CCNA Cyber Operations” of the network academy Cisco is considered. At present, many universities have similar academies, while others can open them. On the basis of free software platforms Apache CloudStack and EVE-NG Community authors designed and implemented a virtual cloud laboratory. It operates according to the “IaaS” model. Thanks to the technology of embedded virtualization, the work of many virtual machines, storing of their status, traffic analysis and visualization of network topologies are maintained. The article describes the experience of teaching students of the specialty “Pedagogical education. ICT” in the course “CCNA Cyber Operations” with the use of virtual cloud laboratories. The authors have been conducted a survey of students who studied at the course. Its purpose was to determine how much they satisfied were with the course. Statistical processing of the results was performed on the basis of the Rasch model using the software MiniSteps.
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Neiderer, Andrew M. Network Visualization Using Xj3D. Fort Belvoir, VA: Defense Technical Information Center, September 2010. http://dx.doi.org/10.21236/ada532062.

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