Статті в журналах: "Network analysis and visualization"

1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Tyner, Sam, François Briatte, and Heike Hofmann. "Network Visualization with ggplot2." R Journal 9, no. 1 (2017): 27. http://dx.doi.org/10.32614/rj-2017-023.

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12

MIKHALEVSKA, Galina, and Vitalii MIKHALEVSKYI. "THE USAGE OF GRAPH THEORY CONCEPTS FOR THE COMPLEX NETWORKS ANALYSIS." Herald of Khmelnytskyi National University 305, no. 1 (February 23, 2022): 59–63. http://dx.doi.org/10.31891/2307-5732-2022-305-1-59-63.

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The article considers and investigates the usage of graph theory concepts for the complex networks analysis. Abstract from their physical nature, the topological properties of these networks are considered, which significantly determine the functioning of networks and are the subject of study of complex networks. Each node of the network can be connected with other nodes by a certain number of connections that may have a direction, or nodes can be connected with each other by symmetrical connections. Also in modern systems of analysis and visualization of networks such concepts as degrees of vertices, ranking, clustering, modularity, algorithms of laying of graphs, etc. are widely used. To calculate the parameters of the network as a whole use the number of nodes, the number of edges, the geodetic distance between nodes, the average distance from one node to another, density – the ratio of the number of edges in the network to the maximum possible number of edges for a given number of nodes; number of triads, diameter of the network (maximum geodetic distance). Structural network analysis includes: click detection (subgroups that are more interconnected than other click nodes); identification of network components; finding bridges (nodes, the removal of which breaks the network into incoherent parts); groups of equivalent nodes (which have the most similar communication profiles). One of the areas of complex networks analysis is their visualization, which allows to obtain important information about the structure and properties of the network without accurate calculations. Software tools for the complex networks analysis support the calculation of all the described parameters of the nodes, the network as a whole, provide its structural analysis and visualization, work with different data formats. For the complex networks analysis, the main visualization tools are described, in the vast majority, freely distributed, free programs.

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13

Armitage, Neil. "The Biographical Network Method." Sociological Research Online 21, no. 2 (May 2016): 165–79. http://dx.doi.org/10.5153/sro.3827.

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This article introduces a network visualization method that enables a thorough analysis of the link between life history and social networks. Network visualizations are generally static, and as such they tend to disguise rather than uncover change and continuity within networks, and the influence that certain events may have on someone's sociability. The Biographical Network (BN) is a mixed method approach combining life story interviews with formal SNA that attempts to overcome the consequences of this lack of dynamism in network visualizations. In the first part of the article the underpinnings of the BN design and the logistics of the method are outlined in relation to a doctoral study on cultural cosmopolitanism. In the second part findings from applying the BN method with 28 young British and Spanish adults living in Madrid and Manchester are used to demonstrate its utility and its limitations for sociological analysis.

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14

Ahmed, Nesreen, and Ryan Rossi. "Interactive Visual Graph Analytics on the Web." Proceedings of the International AAAI Conference on Web and Social Media 9, no. 1 (August 3, 2021): 566–69. http://dx.doi.org/10.1609/icwsm.v9i1.14653.

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We present a web-based network visual analytics platform called GraphVis that combines interactive visualizations with analytic techniques to reveal important patterns and insights for sense making, reasoning, and decision-making. The platform is designed with simplicity in mind and allows users to visualize and explore networks in seconds with a simple drag-and-drop of a graph file into the web browser. GraphVis is fast and flexible, web-based, requires no installation, while supporting a wide range of graph formats as well as state-of-the-art visualization and analytic techniques. In particular, the multi-level network analysis engine of GraphVis gives rise to a variety of new possibilities for exploring, analyzing, and understanding complex networks interactively in real-time. Finally, we also highlight other key aspects including filtering, querying, ranking, manipulating, exporting, partitioning (community/role discovery), as well as tools for dynamic network analysis and visualization, interactive graph generators (including two new block model approaches), and a variety of multi-level network analysis and statistical techniques.

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15

Zelinka, Ivan, Donald Davendra, Roman Jašek, and Roman Šenkerík. "Investigation on Visualization, Analysis, and Control of Complex Networks Dynamics." International Journal of Energy Optimization and Engineering 1, no. 3 (July 2012): 48–73. http://dx.doi.org/10.4018/ijeoe.2012070103.

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In this article the authors discuss a new method of the so-called complex networks dynamics and its visualization by means of so called coupled map lattices method. The main aim of this article is to investigate whether it is possible to visualize complex network dynamics by means of the same method that is used to model spatiotemporal chaos. The authors suggest using coupled map lattices system to simulate complex network so that each site is equal to one vertex of complex network. Interaction between network vertices is in coupled map lattices equal to the strength of mutual influence between system sites. To promote their ideas, two kinds of complex networks dynamics has been selected for visualization, i.e., network with increasing number of vertices and network with constant number of vertices. All results have been properly visualized and explained.

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16

Lestari, Diana Wahyu, and Rita Ambarwati. "Social Network Analysis: Understanding User Behavior in Threads." Kontigensi : Jurnal Ilmiah Manajemen 12, no. 1 (June 11, 2024): 115–24. http://dx.doi.org/10.56457/jimk.v12i1.505.

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The presence of threads has enhanced users' ability to share stories, information and views in greater detail and effectively in the online world. Social media platforms allow users to interact, share content, and connect online. Therefore, the purpose of this research is to create a Social Network Analysis (SNA) network visualization and categorize the data to contribute to the understanding of user interaction on this platform. The research method in this study is the Social Network Analysis (SNA) approach to analyze the relationship between individuals in social networks. The results show that user participation in the Threads platform has a significant positive impact. The data shows a high level of participation, providing an understanding of user engagement in various topics. Social network analysis revealed characteristics of user interactions, such as the number of nodes, average connectedness, and relationship complexity. In addition, word categories and network visualizations provide insights into relationship patterns and topics in conversations.

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17

Nurhaliza, Nurhaliza, Astuti Astuti, Zulfah Zulfah, and Kasman Ediputra. "Analisis Bibliometrik: Fokus Penelitian Metode Scaffolding terhadap Kemampuan Computational Thinking (2016-2022)." MATHEMA: JURNAL PENDIDIKAN MATEMATIKA 6, no. 2 (July 22, 2024): 397. http://dx.doi.org/10.33365/jm.v6i2.3224.

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The aim of this bibliometric analysis is to identify opportunities and trends in research related to the Scaffolding method's impact on Computational Thinking skills. The bibliometric method was used with data collection of articles from 2016-2022 through Google Scholar using the Publish or Perish software, yielding 1,000 articles. The study results showed that 50 articles were published from 2016-2022. Visualizations in the analysis using VOSviewer can be seen in the form of network visualization, overlay visualization, and density visualization. The network visualization map is used to see the interrelationships and clusters of research themes based on keywords. Overlay visualization is used to identify the years when related research themes were conducted. Meanwhile, density visualization is used to analyze research themes that are saturated and those that are still rarely studied. Based on data analysis using VOSviewer, the research trend on Scaffolding dan Computational Thinking from 2016 to 2022 is relatively new and has rarely been conducted previously.

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18

Lin, Xiang. "Information Visualization from the Perspective of Big Data Analysis and Fusion." Scientific Programming 2021 (November 8, 2021): 1–12. http://dx.doi.org/10.1155/2021/8934632.

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In the big data environment, the visualization technique has been increasingly adopted to mine the data on library and information (L&I), with the diversification of data sources and the growth of data volume. The previous research into the information association of L&I visualization network rarely tries to construct such a network or explore the information association of the network. To overcome these defects, this paper explores the visualization of L&I from the perspective of big data analysis and fusion. Firstly, the authors analyzed the topology of the L&I visualization network and calculated the metrics for the construction of L&I visualization topology map. Next, the importance of meta-paths of the L&I visualization network was calculated. Finally, a complex big data L&I visualization network was established, and the associations between information nodes were analyzed in detail. Experimental results verify the effectiveness of the proposed algorithm.

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19

Toyota, Tetsuya, and Hajime Nobuhara. "Analysis and Visualization of Japanese Law Networks Based on Granular Computing -Visual Law: Visualization System of Japanese Law-." Journal of Advanced Computational Intelligence and Intelligent Informatics 14, no. 2 (March 20, 2010): 150–54. http://dx.doi.org/10.20965/jaciii.2010.p0150.

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In order to grasp a perspective of the over 7,000 laws in Japan, and to find the relationships between law and laws, a method of creating a hierarchical network of laws using granular computing, is proposed. The proposed method analyze hierarchical networks by using an index of network science such as degree distribution and closeness centrality. Furthermore, it visualizes the hierarchical structure within the setting of granular computing. Using the JAVA-based language ‘Prefuse,’ a law network visualization system ‘Visual Law’ is implemented, and it is confirmed that users can easily analyze/understand the law network structure using our proposal.

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20

Warner, Jeremy L., Joshua C. Denny, David A. Kreda, and Gil Alterovitz. "Seeing the forest through the trees: uncovering phenomic complexity through interactive network visualization." Journal of the American Medical Informatics Association 22, no. 2 (October 21, 2014): 324–29. http://dx.doi.org/10.1136/amiajnl-2014-002965.

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Abstract Our aim was to uncover unrecognized phenomic relationships using force-based network visualization methods, based on observed electronic medical record data. A primary phenotype was defined from actual patient profiles in the Multiparameter Intelligent Monitoring in Intensive Care II database. Network visualizations depicting primary relationships were compared to those incorporating secondary adjacencies. Interactivity was enabled through a phenotype visualization software concept: the Phenomics Advisor. Subendocardial infarction with cardiac arrest was demonstrated as a sample phenotype; there were 332 primarily adjacent diagnoses, with 5423 relationships. Primary network visualization suggested a treatment-related complication phenotype and several rare diagnoses; re-clustering by secondary relationships revealed an emergent cluster of smokers with the metabolic syndrome. Network visualization reveals phenotypic patterns that may have remained occult in pairwise correlation analysis. Visualization of complex data, potentially offered as point-of-care tools on mobile devices, may allow clinicians and researchers to quickly generate hypotheses and gain deeper understanding of patient subpopulations.

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21

Nuttachot Promrit, Anirach Mingkhwan, Supaporn Simcharoen, and Nati Namvong. "Multi-Dimensional Visualization for Network Forensic Analysis." International Journal of Advancements in Computing Technology 4, no. 5 (March 15, 2012): 222–32. http://dx.doi.org/10.4156/ijact.vol4.issue5.27.

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22

Lee, Taerim. "Visualization of gene data using network analysis." Journal of the Korean Data And Information Science Sociaty 29, no. 6 (November 30, 2018): 1421–43. http://dx.doi.org/10.7465/jkdi.2018.29.6.1421.

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23

Basole, Rahul C., and Marcus A. Bellamy. "Global supply network health: Analysis and visualization." Information Knowledge Systems Management 11, no. 1,2 (2012): 59–76. http://dx.doi.org/10.3233/iks-2012-0173.

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24

Xianlin Hu, Aidong Lu, and Xintao Wu. "Spectrum-Based Network Visualization for Topology Analysis." IEEE Computer Graphics and Applications 33, no. 1 (January 2013): 58–68. http://dx.doi.org/10.1109/mcg.2012.89.

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25

Brown, Kevin R., David Otasek, Muhammad Ali, Michael J. McGuffin, Wing Xie, Baiju Devani, Ian Lawson van Toch, and Igor Jurisica. "NAViGaTOR: Network Analysis, Visualization and Graphing Toronto." Bioinformatics 25, no. 24 (October 16, 2009): 3327–29. http://dx.doi.org/10.1093/bioinformatics/btp595.

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26

Brandes, Ulrik. "Social network analysis and visualization [Applications Corner]." IEEE Signal Processing Magazine 25, no. 6 (November 2008): 147–51. http://dx.doi.org/10.1109/msp.2008.929814.

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27

Zolkepli, Maslina, Fangyan Dong, and Kaoru Hirota. "Visualizing Fuzzy Relationship in Bibliographic Big Data Using Hybrid Approach Combining Fuzzyc-Means and Newman-Girvan Algorithm." Journal of Advanced Computational Intelligence and Intelligent Informatics 18, no. 6 (November 20, 2014): 896–907. http://dx.doi.org/10.20965/jaciii.2014.p0896.

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Bibliographic big data visualization method is proposed by incorporating a combination of fuzzyc-means clustering and the Newman-Girvan clustering algorithm, where clustered results are displayed in a network view by grouping objects with similar cluster memberships. As current bibliographic visualizations focus on the crisp relationship among data, fuzzy analysis and visualization may offer insights to bibliographic big data, enabling faster decision making by improving displayed information precision. The proposed method is applied to the DBLP citation network dataset. Results show that merging two clustering algorithms and visualization using fuzzy techniques enables the user to converge a few target papers within an average of 5 minutes from 1.5 million papers stored in the DBLP. Users targeted for the proposed method include researchers, educators, and students who hope to use real-world social and biological networks. The proposal is planned to be opened to the public through the Internet.

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Lin, Xiang. "Information Visualization from the Perspective of Big Data Analysis and Fusion." WSEAS TRANSACTIONS ON COMPUTERS 20 (November 29, 2021): 352–61. http://dx.doi.org/10.37394/23205.2021.20.37.

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In the big data environment, the visualization technique has been increasingly adopted to mine the data on library and information (L&I), with the diversification of data sources and the growth of data volume. However, there are several defects with the research on information association of L&I visualization network: the lack of optimization of network layout algorithms, and the absence of L&I information fusion and comparison in multiple disciplines, in the big data environment. To overcome these defects, this paper explores the visualization of L&I from the perspective of big data analysis and fusion. Firstly, the authors analyzed the topology of the L&I visualization network, and calculated the metrics for the construction of L&I visualization topology map. Next, the importance of meta-paths of the L&I visualization network was calculated. Finally, a complex big data L&I visualization network was established, and the associations between information nodes were analyzed in details. Experimental results verify the effectiveness of the proposed algorithm

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29

Pavlopoulos, Georgios A., David Paez-Espino, Nikos C. Kyrpides, and Ioannis Iliopoulos. "Empirical Comparison of Visualization Tools for Larger-Scale Network Analysis." Advances in Bioinformatics 2017 (July 18, 2017): 1–8. http://dx.doi.org/10.1155/2017/1278932.

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Gene expression, signal transduction, protein/chemical interactions, biomedical literature cooccurrences, and other concepts are often captured in biological network representations where nodes represent a certain bioentity and edges the connections between them. While many tools to manipulate, visualize, and interactively explore such networks already exist, only few of them can scale up and follow today’s indisputable information growth. In this review, we shortly list a catalog of available network visualization tools and, from a user-experience point of view, we identify four candidate tools suitable for larger-scale network analysis, visualization, and exploration. We comment on their strengths and their weaknesses and empirically discuss their scalability, user friendliness, and postvisualization capabilities.

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30

He, Hui, Guotao Fan, Jianwei Ye, and Weizhe Zhang. "A Topology Visualization Early Warning Distribution Algorithm for Large-Scale Network Security Incidents." Scientific World Journal 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/827376.

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It is of great significance to research the early warning system for large-scale network security incidents. It can improve the network system’s emergency response capabilities, alleviate the cyber attacks’ damage, and strengthen the system’s counterattack ability. A comprehensive early warning system is presented in this paper, which combines active measurement and anomaly detection. The key visualization algorithm and technology of the system are mainly discussed. The large-scale network system’s plane visualization is realized based on the divide and conquer thought. First, the topology of the large-scale network is divided into some small-scale networks by the MLkP/CR algorithm. Second, the sub graph plane visualization algorithm is applied to each small-scale network. Finally, the small-scale networks’ topologies are combined into a topology based on the automatic distribution algorithm of force analysis. As the algorithm transforms the large-scale network topology plane visualization problem into a series of small-scale network topology plane visualization and distribution problems, it has higher parallelism and is able to handle the display of ultra-large-scale network topology.

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31

Chandel,, Aditya. "Real-time Graph Visualization with JavaFX: Exploring Large-scale Network Structures." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (April 20, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem31221.

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The ability to visualize and comprehend complex network structures is crucial in various domains, including social media analysis, computer networks, and transportation systems. However, visualizing large-scale graphs poses significant challenges due to computational limitations and rendering performance constraints. This research paper presents a novel approach to real-time graph visualization using JavaFX, a powerful Java-based framework for developing rich client applications. By leveraging efficient data structures, rendering optimizations, and multithreading techniques, our proposed system achieves real-time visualization of large-scale graphs, enabling users to explore and interact with dynamic network structures seamlessly. The system incorporates advanced layout algorithms and visual encodings to enhance the clarity and interpretability of the visualizations. Extensive experiments were conducted using real-world and synthetic datasets to evaluate the system's performance, scalability, and usability. The results demonstrate the effectiveness of our approach in rendering large graphs in real-time, outperforming existing techniques. Furthermore, a user study was conducted to assess the system's usability and gather feedback on interaction and exploration features, highlighting potential applications in various domains. Keywords— Graph Visualization, Real-time Rendering, JavaFX, Large-scale Networks, Layout Algorithms, Rendering Optimizations, User Interaction, Performance Evaluation.

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Gemici, Kurtuluş, and Anthony Vashevko. "Visualizing Hierarchical Social Networks." Socius: Sociological Research for a Dynamic World 4 (January 1, 2018): 237802311877298. http://dx.doi.org/10.1177/2378023118772982.

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The authors propose a novel technique for the visualization of networks that contain a hierarchical structure: networks in which certain nodes and groups of nodes can be classified through a relation of precedence. Networks with a hierarchical structure frequently arise in sociology and various other disciplines, but the existing methods for visualizing such networks leave much to be desired. The method developed in this work builds on the tradition of visualization in social network analysis; it aims to simultaneously represent the positions of different nodes and the relationships between groups containing the nodes in the network. As such, the proposed visualization method facilitates theoretical and empirical analysis of social structures by algorithmically combining information from the underlying network with the information from the hierarchical structure of the network. The authors illustrate the proposed method with social networks examined through cohesive blocking and k-core decomposition.

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Kong, Xiangjun, Wenxia Zhou, Jian-Bo Wan, Qianru Zhang, Jingyun Ni, and Yuanjia Hu. "An Integrative Thrombosis Network: Visualization and Topological Analysis." Evidence-Based Complementary and Alternative Medicine 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/265303.

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A comprehensive understanding of the integrative nature of the molecular network in thrombosis would be very helpful to develop multicomponent and multitarget antithrombosis drugs for use in traditional Chinese medicine (TCM). This paper attempts to comprehensively map the molecular network in thrombosis by combining platelet signaling, the coagulation cascade, and natural clot dissolution systems and to analyze the topological characteristics of the network, including the centralities of nodes, network modules, and network robustness. The results in this research advance understanding of functions of proteins in the thrombosis network and provide a reference for predicting potential therapeutic antithrombotic targets and evaluating their influence on the network.

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Yang, Jiying, Qi Long, Xiaoyun Zhu, and Yuan Yang. "Application of Bayesian Analysis Based on Neural Network and Deep Learning in Data Visualization." Journal of Electronic Research and Application 8, no. 4 (August 12, 2024): 88–93. http://dx.doi.org/10.26689/jera.v8i4.7924.

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This study aims to explore the application of Bayesian analysis based on neural networks and deep learning in data visualization. The research background is that with the increasing amount and complexity of data, traditional data analysis methods have been unable to meet the needs. Research methods include building neural networks and deep learning models, optimizing and improving them through Bayesian analysis, and applying them to the visualization of large-scale data sets. The results show that the neural network combined with Bayesian analysis and deep learning method can effectively improve the accuracy and efficiency of data visualization, and enhance the intuitiveness and depth of data interpretation. The significance of the research is that it provides a new solution for data visualization in the big data environment and helps to further promote the development and application of data science.

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35

Liu, Yaoxuan. "Analysis of network resilienceon global air transportation." Applied and Computational Engineering 6, no. 1 (June 14, 2023): 67–75. http://dx.doi.org/10.54254/2755-2721/6/20230678.

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Small-world network is a very common network structure, which is characterized by low average degree, small average path length and high centrality. At the same time, small-world networks have high resilience to random errors and low resilience to targeted attacks. In this study, the importance of nodes is represented by attributes such as degree and centrality, and attacks refer to the removal of important nodes. The network is attacked according to the degree, betweenness and closeness centrality to observe the power distribution. The data is mainly obtained from the open source OpenFlight. Gephi, Python, and Excel are used as tools. Gephi is used for network visualization and analysis. The third-party python libraries Pandas, Matplotlib, and NetworkX were used in this study to deal with the things that Gephi can't compute or represent well, and then plot the corresponding graphs with Matplotlib. The work of cleaning data is mainly done by excel.

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Goodall, John R. "An Evaluation of Visual and Textual Network Analysis Tools." Information Visualization 10, no. 2 (April 2011): 145–57. http://dx.doi.org/10.1057/ivs.2011.2.

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User testing is an integral component of user-centered design, but has only rarely been applied to visualization for cyber security applications. This article presents the results of a comparative evaluation between a visualization-based application and a more traditional, table-based application for analyzing computer network packet captures. We conducted this evaluation as part of the user-centered design process. Participants performed both structured, well-defined tasks and exploratory, open-ended tasks with both tools. We measured accuracy and efficiency for the well-defined tasks, number of insights was measured for exploratory tasks and user perceptions were recorded for each tool. The results of this evaluation demonstrated that users performed significantly more accurately in the well-defined tasks, discovered a higher number of insights and demonstrated a clear preference for the visualization tool. The study design presented may be useful for future researchers performing user testing on visualization for cyber security applications.

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Summers, Kenneth L., Thomas Preston Caudell, Kathryn Berkbigler, Brian Bush, Kei Davis, and Steve Smith. "Graph Visualization for the Analysis of the Structure and Dynamics of Extreme-Scale Supercomputers." Information Visualization 3, no. 3 (July 8, 2004): 209–22. http://dx.doi.org/10.1057/palgrave.ivs.9500079.

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We are exploring the development and application of information visualization techniques for the analysis of new massively parallel supercomputer architectures. Modern supercomputers typically comprise very large clusters of commodity SMPs interconnected by possibly dense and often non-standard networks. The scale, complexity, and inherent non-locality of the structure and dynamics of this hardware, and the operating systems and applications distributed over them, challenge traditional analysis methods. As part of the á la carte (A Los Alamos Computer Architecture Toolkit for Extreme-Scale Architecture Simulation) team at Los Alamos National Laboratory, who are simulating these new architectures, we are exploring advanced visualization techniques and creating tools to enhance analysis of these simulations with intuitive three-dimensional representations and interfaces. This work complements existing and emerging algorithmic analysis tools. In this paper, we give background on the problem domain, a description of a prototypical computer architecture of interest (on the order of 10,000 processors connected by a quaternary fat-tree communications network), and a presentation of three classes of visualizations that clearly display the switching fabric and the flow of information in the interconnecting network.

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Lee, Seung-Joo. "Mobile Communication Technology Analysis Using Network Visualization Model." Journal of Korean Institute of Intelligent Systems 29, no. 6 (December 31, 2019): 445–51. http://dx.doi.org/10.5391/jkiis.2019.29.6.445.

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Curtis, Ross E., Jing Xiang, Ankur Parikh, Peter Kinnaird, and Eric P. Xing. "Enabling dynamic network analysis through visualization in TVNViewer." BMC Bioinformatics 13, no. 1 (2012): 204. http://dx.doi.org/10.1186/1471-2105-13-204.

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Schwarz, Roland, Chunguang Liang, Christoph Kaleta, Mark Kühnel, Eik Hoffmann, Sergei Kuznetsov, Michael Hecker, Gareth Griffiths, Stefan Schuster, and Thomas Dandekar. "Integrated network reconstruction, visualization and analysis using YANAsquare." BMC Bioinformatics 8, no. 1 (2007): 313. http://dx.doi.org/10.1186/1471-2105-8-313.

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41

Salavert, Francisco, Luz García-Alonso, Rubén Sánchez, Roberto Alonso, Marta Bleda, Ignacio Medina, and Joaquín Dopazo. "Web-based network analysis and visualization using CellMaps." Bioinformatics 32, no. 19 (June 13, 2016): 3041–43. http://dx.doi.org/10.1093/bioinformatics/btw332.

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42

Andriotis, Panagiotis, Zacharias Tzermias, Anthi Mparmpaki, Sotiris Ioannidis, and George Oikonomou. "Multilevel Visualization Using Enhanced Social Network Analysis with Smartphone Data." International Journal of Digital Crime and Forensics 5, no. 4 (October 2013): 34–54. http://dx.doi.org/10.4018/ijdcf.2013100103.

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While technology matures and becomes more productive, mobile devices can be affordable and, consequently, fully integrated in people's lives. After their unexpected bloom and acceptance, Online Social Networks are now sources of valuable information. The authors therefore use them for tasks varying from direct marketing to forensic analysis. The authors have already seen Social Network Forensics techniques focused on particular networks implementing methods that collect data from user accounts. During the forensic analysis it is common to aggregate information from different sources but, usually, this procedure causes correlation problems. Here, the authors present their method to correlate data gathered from various social networks in combination with smartphones creating a new form of social map of the user under investigation. In addition, the authors introduce a multi level graph that utilises the correlated information from the smartphone and the social networks and demonstrates in three dimensions the relevance of each contact with the suspect.

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43

Haider, Johanna Doppler, Patrick Seidler, Margit Pohl, Neesha Kodagoda, Rick Adderley, and B. L. William Wong. "How Analysts Think: Sense-making Strategies in the Analysis of Temporal Evolution and Criminal Network Structures and Activities." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 61, no. 1 (September 2017): 193–97. http://dx.doi.org/10.1177/1541931213601532.

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Analysis of criminal activity based on offenders’ social networks is an established procedure in intelligence analysis. The complexity of the data poses an obstacle for analysts to gauge network developments, e.g. detect emerging problems. Visualization is a powerful tool to achieve this, but it is essential to know how the analysts’ sense-making strategies can be supported most efficiently. Based on a think aloud study we identified ten cognitive strategies on a general level to be useful for designers. We also provide some examples how these strategies can be supported through appropriate visualizations.

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LANDE, D., and L. STRASHNOY. "Formation of networks of concepts in the field of law with the help of an artificial intelligence system." INFORMATION AND LAW, no. 2(45) (May 30, 2023): 88–93. http://dx.doi.org/10.37750/2616-6798.2023.2(45).282326.

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The article describes a methodology for constructing networks of concepts related to legal documents and the subject area of “Information Law”. The methodology is based on the use of the artificial intelligence system ChatGPT and the Gephi network analysis and visualization program. The article demonstrates the possibility of constructing concept networks using ChatGPT, solving tasks that previously required large time and human resources. It shows how tools of intelligent text analytics and network analysis are integrated, as well as their visualization. The article proposes emulating multiple experts by repeatedly querying the ChatGPT system. The methodology can be applied to network analysis of documentary sources and the construction of subject area models.

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45

Pokorny, Jennifer J., Alex Norman, Anthony P. Zanesco, Susan Bauer-Wu, Baljinder K. Sahdra, and Clifford D. Saron. "Network analysis for the visualization and analysis of qualitative data." Psychological Methods 23, no. 1 (March 2018): 169–83. http://dx.doi.org/10.1037/met0000129.

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46

Widiastiana Vista Wijaya, Citra Puspa Maulidina, Shilvy Andini Sunarto, and Siti Zulzilah. "ANALISIS BIBLIOMETRIK TERHADAP TREND RISET “HALAL TOURISM” MENGGUNAKAN VOSVIEWER." Jurnal Sosial Humaniora dan Pendidikan 2, no. 3 (November 26, 2023): 127–38. http://dx.doi.org/10.56127/jushpen.v2i3.1168.

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The aim of bibliometric analysis is mapping research trends by processing metadata from Google Scholar. This research also wants to know the research trend on "Halal Tourism" which uses the Social Network Analysis method. Research through searching the Google Scholar database with the keywords "Halal Tourism". Retrieval of metadata using the Publish or Perish (POP) application. The resulting data from PoP is then analyzed descriptively based on the year the publication was published, publisher name, researcher productivity and journal ranking. To obtain an accurate research development map, PoP data was exported to Excel CSV file format and result as RIS, CSV data was made into a private table and RIS data was analyzed using the VOSviewer (VV) application. Article search results show that the number of research publications in 2013 - 2023 was 997 papers. This research is visualized into a network and explained into 3 visualizations, namely Network Visualization, Overlay Visualization, Density Visualization. The results of this visualization can be used as a reference or as important information for researchers to see research trends on "Halal Tourism" and opportunities related to this topic in the future.

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47

Muharam, Riki Satia, Budiman Rusli, Heru Nurasa, and Entang Adhy Muhtar. "Seven clusters of data visualization articles in Scopus using social network analysis." International Journal of Data and Network Science 7, no. 3 (2023): 1333–40. http://dx.doi.org/10.5267/j.ijdns.2023.4.005.

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The aim of this study was to analyse the bibliographic characteristics and content of articles on Data Visualization published in journals indexed by Scopus written by researchers from throughout the world. We conducted a bibliometric and content analysis of publication in the Scopus database. We only retrieved articles written in English. We conducted content analysis using the VOSviewer software and visualized the co-occurrence of keywords and bibliographic coupling of sources and countries. Following the study protocol, we found 862 articles on Data Visualization over the past 30 years. The most productive journal that published these articles was Lecture Notes In Computer Science (n=32). The most productive country was the United States (n=305). Based on citations, the most influential authors, and journals were Thorvaldsdóttir et al., (2013) [Thorvaldsdóttir, H., Robinson, J. T., & Mesirov, J. P. (2013). Integrative Genomics Viewer (IGV): High-performance genomics data visualization and exploration. Briefings in Bioinformatics, 14(2), 178–192.] (n=4699), and IEE Transactions on Visualization and Computer Graphics (n=656). The keywords of research on Data Visualization formed 7 clusters (e.g. Data Visualization, Visualization, and Human). From a global perspective, Data Visualization research in the past 30 years has increased significantly. There were European published journals nominated publications. Thus, Asian countries need to conduct more active research on this topic.

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Wibisono, Eristian. "Discovering Key Actors and Opinion Leaders on Twitter’s Start-Up and Entrepreneurship Topics Trending: A Social Network Analysis Approach." JURNAL IPTEKKOM Jurnal Ilmu Pengetahuan & Teknologi Informasi 25, no. 1 (June 30, 2023): 37–56. http://dx.doi.org/10.17933/iptekkom.25.1.2023.37-56.

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The topic of start-ups and entrepreneurship has been widely discussed on social media to disseminate information while serving as a digital marketing platform. Unfortunately, researchers and practitioners interested in analyzing social networks to gain business advantage through digital platforms still need to be expanded. This paper analyzes networks on social media to find important actors and opinion leaders on trending start-up and entrepreneurship topics. Data analytics is performed using one of the most extensive social media, Twitter. Several visualizations of the social network of these actors are then displayed using specific network visualization applications. The results are discussed by analyzing the main attributes in the network, namely degree, centrality, modularity, and word cloud, which can lead us to discover important attributes in the network: main actors, opinion leaders, groupings of actors according to topics discussed, and specific terms that are trending topics. This paper contributes to digital marketing practitioners and social network analysis studies in related fields with a concise and duplicable methodology. Further research is recommended to work more extensively with more representative data from multiple networks to analyze these topics in more depth.

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Xu, Dingjiang. "Application and Feasibility Analysis of Network Models and Data Analysis in Combating Organized Crime." Lecture Notes in Education Psychology and Public Media 37, no. 1 (January 15, 2024): 185–93. http://dx.doi.org/10.54254/2753-7048/37/20240538.

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This article discusses and analyzes in detail the development potential and application of big data mining and network visualization analysis in combating organized crime. Firstly, the article emphasizes the importance of combating criminal gangs and organized crime in modern society, comparing them to the foundation of stable national development and the cornerstone of national progress. Then it explores the security challenges faced by modern society and more issues related to organized crime, including the problem of crime caused by the wealth gap caused by urbanization and economic development, as well as the outdated technology for cracking down on existing security crimes. This article provides a detailed introduction to the potential of big data mining and network visualization, as well as the advantages of these technologies in practical applications. It covers the collection, processing, sharing, and application of data. At the same time, network visualization is used to visualize crime data, helping decision-makers better understand the current situation and problems of public security and organized crime. The article also discusses the application of artificial intelligence and mobile internet technology in combating organized crime. And the potential of big data in improving urban governance. Finally, this article looks ahead and emphasizes that big data mining and network visualization analysis will continue to play an important role in optimizing social security, indicating the broad prospects of these technologies in the future transportation field.

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Gavrilovic, Nadja, Vladimir Ciric, and Nikola Lozo. "Snort ids system visualization interface for alert analysis." Serbian Journal of Electrical Engineering 19, no. 1 (2022): 67–78. http://dx.doi.org/10.2298/sjee2201067g.

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Over the past decades, the rapid Internet development and the growth in the number of its users have raised various security issues. Therefore, it is of great importance to ensure the security of the network in order to enable the safe exchange of confidential data, as well as their integrity. One of the most important components of network attack detection is an Intrusion Detection System (IDS). Snort IDS is a widely used intrusion detection system, which logs alerts after detecting potentially dangerous network packets. A major challenge in network monitoring is the high volume of generated IDS alerts. A necessary step in successful network protection is the analysis of the great amount of logged alerts in search of deviations from normal traffic that may indicate an intrusion. The goal of this paper is to design and implement a visualization interface for IDS alert analysis, which graphically presents alerts generated by Snort IDS. Also, the proposed system classifies the alerts according to the most important attack parameters, and allows the users to understand evolving network situations and easily detect possible traffic irregularities. An environment in which the system has been tested in real-time is described, and the results of attack detection and classification are given. One of the detected attacks is analyzed in detail, as well as the method of its detection and its possible consequences.

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