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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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Nguyen, Neal Huynh. "Logging, Visualization, and Analysis of Network and Power Data of IoT Devices." DigitalCommons@CalPoly, 2018. https://digitalcommons.calpoly.edu/theses/1990.
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There are approximately 23.14 billion IoT(Internet of Things) devices currently in use worldwide. This number is projected to grow to over 75 billion by 2025. Despite their ubiquity little is known about the security and privacy implications of IoT devices. Several large-scale attacks against IoT devices have already been recorded.
To help address this knowledge gap, we have collected a year’s worth of network traffic and power data from 16 common IoT devices. From this data, we show that we can identify different smart speakers, like the Echo Dot, from analyzing one minute of power data on a shared power line.
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Ribler, Randy L. "Visualizing Categorical Time Series Data with Applications to Computer and Communications Network Traces." Diss., Virginia Tech, 1997. http://hdl.handle.net/10919/30314.
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Visualization tools allow scientists to comprehend very large data sets and to discover relationships which are otherwise difficult to detect. Unfortunately, not all types of data can be visualized easily using existing tools. In particular, long sequences of nonnumeric data cannot be visualized adequately. Examples of this type of data include trace files of computer performance information, the nucleotides in a genetic sequence, a record of stocks traded over a period of years, and the sequence of words in this document. The term categorical time series is defined and used to describe this family of data.
When visualizations designed for numerical time series are applied to categorical time series, the distortions which result from the arbitrary conversion of unordered categorical values to totally ordered numerical values can be profound. Examples of this phenomenon are presented and explained.
Several new, general purpose techniques for visualizing categorical time series data have been developed as part of this work and have been incorporated into the Chitra perfor- mance analysis and visualization system. All of these new visualizations can be produced in O(n) time. The new visualizations for categorical time series provide general purpose techniques for visualizing aspects of categorical data which are commonly of interest. These include periodicity, stationarity, cross-correlation, autocorrelation, and the detection of recurring patterns.
The effective use of these visualizations is demonstrated in a number of application domains, including performance analysis, World Wide Web traffic analysis, network routing simulations, document comparison, pattern detection, and the analysis of the performance of genetic algorithms.Ph. D.
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Liu, Zhicheng. "Network-based visual analysis of tabular data." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/43687.
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Tabular data is pervasive in the form of spreadsheets and relational databases. Although tables often describe multivariate data without explicit network semantics, it may be advantageous to explore the data modeled as a graph or network for analysis. Even when a given table design conveys some static network semantics, analysts may want to look at multiple networks from different perspectives, at different levels of abstraction, and with different edge semantics.
This dissertation is motivated by the observation that a general approach for performing multi-dimensional and multi-level network-based visual analysis on multivariate tabular data is necessary. We present a formal framework based on the relational data model that systematically specifies the construction and transformation
of graphs from relational data tables. In the framework, a set of relational operators provide the basis for rich expressive power for network modeling.
Powered by this relational algebraic framework, we design and implement a visual analytics system called Ploceus. Ploceus supports flexible construction and transformation of networks through a direct manipulation interface, and integrates dynamic network manipulation with visual exploration for a seamless analytic experience.
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Harrison, Irving. "NEXT GENERATION DATA VISUALIZATION AND ANALYSIS FOR SATELLITE, NETWORK, AND GROUND STATION OPERATIONS." International Foundation for Telemetering, 1999. http://hdl.handle.net/10150/607303.
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International Telemetering Conference Proceedings / October 25-28, 1999 / Riviera Hotel and Convention Center, Las Vegas, NevadaRecent years have seen a sharp rise in the size of satellite constellations. The monitoring and analysis tools in use today, however, were developed for smaller constellations and are ill-equipped to handle the increased volume of telemetry data. A new technology that can accommodate vast quantities of data is 3-D visualization. Data is abstracted to show the degree to which it deviates from normal, allowing an analyst to absorb the status of thousands of parameters in a single glance. Trend alarms notify the user of dangerous trends before data exceeds normal limits. Used appropriately, 3-D visualization can extend the life of a satellite by ten to twenty percent.
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Ottenby, Nore. "Focal Operations with Network Distance Based Neighbourhoods : Implementation, Application and Visualization." Thesis, KTH, Geoinformatik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-170018.
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In spatial analysis, many operations are performed considering the neighbouring locations of a feature. The standard definition of a neighbourhood is an area confined by geometrical length and direction with respect to its focus. When allocating a location for a service, the population distribution is often considered. Standard GIS software includes tools for computations with uniform neighbourhoods, usually equal sized circles. These tools can be used for distribution analysis. Many geographic studies used as basis for city planning decisions use distance as an evaluator. It is a frequent occurrence that the actual distance is approximated using factored straight-line distance. For great distances and large datasets, this is a sufficient means of evaluation, whilst for smaller distances for specific locations, it poses major drawbacks. For distribution analysis in a network space, the neighbourhood would need to be derived from the local set of network features, creating a unique neighbourhood for each location. The neighbourhood can then be used to overlay other datasets to perform analysis of features within the network space. This report describes the application of network distance based neighbourhoods to design a tool, Network Location Analysis, for calculating focal statistics for use as a city planning decision support. The tool has been implemented as a workflow of ArcGIS tools scripted as a Python toolbox. The input required by the tool is a population point layer and a vector network dataset. The output is a grid of points with population statistics as attributes and corresponding neighbourhoods generalized as polygons. The tool has been tested by comparing it to standard focal operations implemented in ArcGIS and by applying it to the dataset used when conducting a study on the location of a new metro station using conventional ArcGIS tools. The results have been analysed and visualized and compared to data used in the study. The result concludes that Network Location Analysis surpasses conventional ArcGIS tools when conducting analysis on features in a network. It derives an accurate set of sum and proximity statistics for all locations within the processing extent, enabling analysis on the population distribution throughout the area and for specific points. The output is intuitive, manageable and can be visualized as raster or by displaying the neighbourhoods as polygons and can be used to evaluate population distribution and network connectivity. The drawbacks of the tool are its lack of robustness, its rigidity to input and the inefficient implementation causing execution time to be unpractical.
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Prohaska, Steffen. "Skeleton-based visualization of massive voxel objects with network-like architecture." Phd thesis, Universität Potsdam, 2007. http://opus.kobv.de/ubp/volltexte/2007/1488/.
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This work introduces novel internal and external memory algorithms for computing voxel skeletons of massive voxel objects with complex network-like architecture and for converting these voxel skeletons to piecewise linear geometry, that is triangle meshes and piecewise straight lines. The presented techniques help to tackle the challenge of visualizing and analyzing 3d images of increasing size and complexity, which are becoming more and more important in, for example, biological and medical research.
Section 2.3.1 contributes to the theoretical foundations of thinning algorithms with a discussion of homotopic thinning in the grid cell model. The grid cell model explicitly represents a cell complex built of faces, edges, and vertices shared between voxels. A characterization of pairs of cells to be deleted is much simpler than characterizations of simple voxels were before. The grid cell model resolves topologically unclear voxel configurations at junctions and locked voxel configurations causing, for example, interior voxels in sets of non-simple voxels. A general conclusion is that the grid cell model is superior to indecomposable voxels for algorithms that need detailed control of topology.
Section 2.3.2 introduces a noise-insensitive measure based on the geodesic distance along the boundary to compute two-dimensional skeletons. The measure is able to retain thin object structures if they are geometrically important while ignoring noise on the object's boundary. This combination of properties is not known of other measures. The measure is also used to guide erosion in a thinning process from the boundary towards lines centered within plate-like structures. Geodesic distance based quantities seem to be well suited to robustly identify one- and two-dimensional skeletons. Chapter 6 applies the method to visualization of bone micro-architecture.
Chapter 3 describes a novel geometry generation scheme for representing voxel skeletons, which retracts voxel skeletons to piecewise linear geometry per dual cube. The generated triangle meshes and graphs provide a link to geometry processing and efficient rendering of voxel skeletons. The scheme creates non-closed surfaces with boundaries, which contain fewer triangles than a representation of voxel skeletons using closed surfaces like small cubes or iso-surfaces. A conclusion is that thinking specifically about voxel skeleton configurations instead of generic voxel configurations helps to deal with the topological implications. The geometry generation is one foundation of the applications presented in Chapter 6.
Chapter 5 presents a novel external memory algorithm for distance ordered homotopic thinning. The presented method extends known algorithms for computing chamfer distance transformations and thinning to execute I/O-efficiently when input is larger than the available main memory. The applied block-wise decomposition schemes are quite simple. Yet it was necessary to carefully analyze effects of block boundaries to devise globally correct external memory variants of known algorithms. In general, doing so is superior to naive block-wise processing ignoring boundary effects. Chapter 6 applies the algorithms in a novel method based on confocal microscopy for quantitative study of micro-vascular networks in the field of microcirculation.Die vorliegende Arbeit führt I/O-effiziente Algorithmen und Standard-Algorithmen zur Berechnung von Voxel-Skeletten aus großen Voxel-Objekten mit komplexer, netzwerkartiger Struktur und zur Umwandlung solcher Voxel-Skelette in stückweise-lineare Geometrie ein. Die vorgestellten Techniken werden zur Visualisierung und Analyse komplexer drei-dimensionaler Bilddaten, beispielsweise aus Biologie und Medizin, eingesetzt. Abschnitt 2.3.1 leistet mit der Diskussion von topologischem Thinning im Grid-Cell-Modell einen Beitrag zu den theoretischen Grundlagen von Thinning-Algorithmen. Im Grid-Cell-Modell wird ein Voxel-Objekt als Zellkomplex dargestellt, der aus den Ecken, Kanten, Flächen und den eingeschlossenen Volumina der Voxel gebildet wird. Topologisch unklare Situationen an Verzweigungen und blockierte Voxel-Kombinationen werden aufgelöst. Die Charakterisierung von Zellpaaren, die im Thinning-Prozess entfernt werden dürfen, ist einfacher als bekannte Charakterisierungen von so genannten "Simple Voxels". Eine wesentliche Schlussfolgerung ist, dass das Grid-Cell-Modell atomaren Voxeln überlegen ist, wenn Algorithmen detaillierte Kontrolle über Topologie benötigen. Abschnitt 2.3.2 präsentiert ein rauschunempfindliches Maß, das den geodätischen Abstand entlang der Oberfläche verwendet, um zweidimensionale Skelette zu berechnen, welche dünne, aber geometrisch bedeutsame, Strukturen des Objekts rauschunempfindlich abbilden. Das Maß wird im weiteren mit Thinning kombiniert, um die Erosion von Voxeln auf Linien zuzusteuern, die zentriert in plattenförmigen Strukturen liegen. Maße, die auf dem geodätischen Abstand aufbauen, scheinen sehr geeignet zu sein, um ein- und zwei-dimensionale Skelette bei vorhandenem Rauschen zu identifizieren. Eine theoretische Begründung für diese Beobachtung steht noch aus. In Abschnitt 6 werden die diskutierten Methoden zur Visualisierung von Knochenfeinstruktur eingesetzt. Abschnitt 3 beschreibt eine Methode, um Voxel-Skelette durch kontrollierte Retraktion in eine stückweise-lineare geometrische Darstellung umzuwandeln, die als Eingabe für Geometrieverarbeitung und effizientes Rendering von Voxel-Skeletten dient. Es zeigt sich, dass eine detaillierte Betrachtung der topologischen Eigenschaften eines Voxel-Skeletts einer Betrachtung von allgemeinen Voxel-Konfigurationen für die Umwandlung zu einer geometrischen Darstellung überlegen ist. Die diskutierte Methode bildet die Grundlage für die Anwendungen, die in Abschnitt 6 diskutiert werden. Abschnitt 5 führt einen I/O-effizienten Algorithmus für Thinning ein. Die vorgestellte Methode erweitert bekannte Algorithmen zur Berechung von Chamfer-Distanztransformationen und Thinning so, dass diese effizient ausführbar sind, wenn die Eingabedaten den verfügbaren Hauptspeicher übersteigen. Der Einfluss der Blockgrenzen auf die Algorithmen wurde analysiert, um global korrekte Ergebnisse sicherzustellen. Eine detaillierte Analyse ist einer naiven Zerlegung, die die Einflüsse von Blockgrenzen vernachlässigt, überlegen. In Abschnitt 6 wird, aufbauend auf den I/O-effizienten Algorithmen, ein Verfahren zur quantitativen Analyse von Mikrogefäßnetzwerken diskutiert.
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Maushagen, Jan. "Visual Analysis of Publication Networks." Thesis, Linnéuniversitetet, Institutionen för datavetenskap (DV), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-27487.
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This thesis documents the development of a web-application attacking the problem of visualization of co-authorship networks. The visualization encompasses several views.Each of them shows different aspects of the data which is loaded from Academic Archive Online (DiVa), a library system which holds all publications released in the Linnaeus University. To detect relationships among authors, a new interactive layout for Node-Link Diagrams was developed which shows publications, authors and corresponding organizations (faculties, departments) in a radial manner. This Network-View is connected to another view showing the attributes (year, type) of the publications. In development, particular emphasis was placed on a rich support of user interaction in order to equip the user with a tool that allows graphical and explorative analysis of the underlying data.
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Blume, Hans Michael [Verfasser], and C. [Akademischer Betreuer] Weinhardt. "Behavior Identification in Markets - using Visualization and Network Analysis / Hans Michael Blume. Betreuer: C. Weinhardt." Karlsruhe : KIT-Bibliothek, 2012. http://d-nb.info/1020230096/34.
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Miller, Paul Michael. "Visualization for network forensic analyses extending the Forensic Log Investigator (FLI) /." [Ames, Iowa : Iowa State University], 2008.
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xinjian, qi. "COMPUTATIONAL ANALYSIS, VISUALIZATION AND TEXT MINING OF METABOLIC NETWORKS." Case Western Reserve University School of Graduate Studies / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=case1378479338.
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Zhang, Yinghua. "Stock Market Network Topology Analysis Based on a Minimum Spanning Tree Approach." Bowling Green State University / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1245347181.
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García, Sara. "Visualization of Learning Paths as Networks of Topics." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-100683.
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Nowadays, interactive visualizations have been one of the most used tools in Big Data fields for the purpose of searching for relationships and structured information in large datasets of unstructured information. In this project, these tools are applied to extract structured information from students following Self-Regulated Learning (SRL). By means of an interactive graph, we are able to study the paths that the students follow in the learning materials. Our visualization supports the investigation of patterns of behaviour of the students, which later could be used, for example, to adapt the study program to the student’s needs in a dynamic way or offer guidance if necessary.
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Fung, David Cho Yau. "Visualization and analysis of gene expression in bio-molecular networks." Phd thesis, Faculty of Engineering and Information Technologies, 2010. http://hdl.handle.net/2123/9325.
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Fabbri, Renato. "Topological stability and textual differentiation in human interaction networks: statistical analysis, visualization and linked data." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/76/76132/tde-11092017-154706/.
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This work reports on stable (or invariant) topological properties and textual differentiation in human interaction networks, with benchmarks derived from public email lists. Activity along time and topology were observed in snapshots in a timeline, and at different scales. Our analysis shows that activity is practically the same for all networks across timescales ranging from seconds to months. The principal components of the participants in the topological metrics space remain practically unchanged as different sets of messages are considered. The activity of participants follows the expected scale-free outline, thus yielding the hub, intermediary and peripheral classes of vertices by comparison against the Erdös-Rényi model. The relative sizes of these three sectors are essentially the same for all email lists and the same along time. Typically, 3-12% of the vertices are hubs, 15-45% are intermediary and 44-81% are peripheral vertices. Texts from each of such sectors are shown to be very different through direct measurements and through an adaptation of the Kolmogorov-Smirnov test. These properties are consistent with the literature and may be general for human interaction networks, which has important implications for establishing a typology of participants based on quantitative criteria. For guiding and supporting this research, we also developed a visualization method of dynamic networks through animations. To facilitate verification and further steps in the analyses, we supply a linked data representation of data related to our results.Este trabalho relata propriedades topológicas estáveis (ou invariantes) e diferenciação textual em redes de interação humana, com referências derivadas de listas públicas de e-mail. A atividade ao longo do tempo e a topologia foram observadas em instantâneos ao longo de uma linha do tempo e em diferentes escalas. A análise mostra que a atividade é praticamente a mesma para todas as redes em escalas temporais de segundos a meses. As componentes principais dos participantes no espaço das métricas topológicas mantêm-se praticamente inalteradas quando diferentes conjuntos de mensagens são considerados. A atividade dos participantes segue o esperado perfil livre de escala, produzindo, assim, as classes de vértices dos hubs, dos intermediários e dos periféricos em comparação com o modelo Erdös-Rényi. Os tamanhos relativos destes três setores são essencialmente os mesmos para todas as listas de e-mail e ao longo do tempo. Normalmente, 3-12% dos vértices são hubs, 15-45% são intermediários e 44-81% são vértices periféricos. Os textos de cada um destes setores são considerados muito diferentes através de uma adaptação dos testes de Kolmogorov-Smirnov. Estas propriedades são consistentes com a literatura e podem ser gerais para redes de interação humana, o que tem implicações importantes para o estabelecimento de uma tipologia dos participantes com base em critérios quantitativos. De modo a guiar e apoiar esta pesquisa, também desenvolvemos um método de visualização para redes dinâmicas através de animações. Para facilitar a verificação e passos seguintes nas análises, fornecemos uma representação em dados ligados dos dados relacionados aos nossos resultados.
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Renoust, Benjamin. "Analysis and Visualisation of Edge Entanglement in Multiplex Networks." Phd thesis, Université Sciences et Technologies - Bordeaux I, 2013. http://tel.archives-ouvertes.fr/tel-00942358.
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When it comes to comprehension of complex phenomena, humans need to understand what interactions lie within them.These interactions are often captured with complex networks. However, the interaction pluralism is often shallowed by traditional network models. We propose a new way to look at these phenomena through the lens of multiplex networks, in which catalysts are drivers of the interaction through substrates. To study the entanglement of a multiplex network is to study how edges intertwine, in other words, how catalysts interact. Our entanglement analysis results in a full set of new objects which completes traditional network approaches: the entanglement homogeneity and intensity of the multiplex network, and the catalyst interaction network, with for each catalyst, an entanglement index. These objects are very suitable for embedment in a visual analytics framework, to enable comprehension of a complex structure. We thus propose of visual setting with coordinated multiple views. We take advantage of mental mapping and visual linking to present simultaneous information of a multiplex network at three different levels of abstraction. We complete brushing and linking with a leapfrog interaction that mimics the back-and-forth process involved in users' comprehension. The method is validated and enriched through multiple applications including assessing group cohesion in document collections, and identification of particular associations in social networks.
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Fenn, Edward, and Eric Fornling. "Mapping and identifying misplaced devices on a network by use of metadata." Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-14687.
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Context. Network placement of devices is an issue of operational security for most companies today. Since a misplaced device can compromise an entire network and in extension, a company, it is essential to keep track of what is placed where. Knowledge is the key to success, and knowing your network is required to make it secure. Large networks however may be hard to keep track of, since employees can connect or unplug devices and make it hard for the administrators to keep updated on the network at all times. Objectives. This analysis focuses on the creation of an analysis method for network mapping based on metadata. This analysis method is to be implemented in a tool that automatically maps a network based on specific metadata attributes. The motivation and goal for this study is to create a method that will improve network mapping with regard to identifying misplaced devices, and to achieve a better understanding of the impact misplaced systems can have on a network. Method. The method for analyzing the metadata was manually checking the network metadata that was gathered by Outpost24 AB’s proprietary vulnerability scanner. By analyzing this metadata, certain attributes were singled out as necessary for the identification. These attributes were then implemented in a probability function that based on the information determines the device type. The results from the probability function are then presented visually as a network graph. A warning algorithm was then run against these results and prompted warnings when finding misplaced devices on subnets. Results. The proposed method is deemed to be 30 878 times faster than the previous method, i.e. the manual control of metadata. It is however not as accurate with an identification rate of between 80% and 93% of devices and correct device type identification of 95-98% of the identified devices. This is as opposed to the previous method, i.e. the manual control of metadata, with 80-93%% identification rate and 100% correct device type identification. The proposed method also flagged 48.9% of the subnets as misconfigured. Conclusion. In conclusion, the proposed method proves that it is indeed possible to identify misplaced devices on networks based on metadata analysis. The proposed method is also considerably faster than the previous method, but does need some further work to be as efficient as the previous method and reach a 100% device type identification rate.Kontext. Placeringen av enheter i nätverk har idag blivit en säkerhetsfråga för de flesta företagen. Eftersom en felplacerad enhet kan äventyra ett helt nätverk, och i förlängning, ett företag så är det essentiellt att ha koll på vad som är placerat vart. Kunskap är nyckeln till framgång, och att ha kunskap om sin nätverksstruktur är avgörande för att göra nätverket säkert. Stora nätverk kan dock vara svåra att ha koll på om anställda kan lägga till eller ta bort enheter, och på så sätt göra det svårt för administratören att ständigt hålla sig uppdaterad om vad som finns vart. Mål. Den här studien fokuserar på skapandet av en analysmetod för att kartlägga ett nätverk baserat på metadata från nätverket. Analysmetoden ska sedan implementeras i ett verktyg som sedan automatiskt kartlägger nätverket utifrån den metadata som valts ut i analysmetoden. Motivationen och målet med den här studien är att skapa en metod som förbättrar nätverkskartläggning med syftet att identifiera felplacerade enheter, och att uppnå en större förståelse för den inverkan felplacerade enheter kan få för ett nätverk. Metod. Metoden för att analysera metadatan var att genom att för hand leta igenom den metadata som Outpost24 ABs sårbarhetsskanner samlade in när den letade efter sårbarheter i ett nätverk. Genom att analysera metadatan så kunde vi singla ut enskilda bitar som vi ansåg vara nödvändiga för att identifiera enhetens typ. Dessa attribut implementerades sedan i en sannolikhetsfunktion som avgjorde vilken typ en enhet hade, baserat på informationen i metadatan. Resultatet från denna sannolikhetsfunktion presenterades sedan visuellt som en graf. En algoritm som matade ut varningar om den hittade felkonfigurerade subnät kördes sedan mot resultaten från sannolikhetsfunktionen. Resultat. Den i den här rapporten föreslagna metoden är fastställt till att vara cirka 30 878 gånger snabbare än föregående metoder, i.e. att leta igenom metadatan för hand. Dock så är den föreslagna metoden inte lika exakt då den har en identifikationsgrad på 80-93% av enheterna på nätverket, och en korrekt identifikationsgrad på enhetstypen på 95-98% av de identifierade enheterna. Detta till skillnad från den föregående metoden som hade 80-93% respektive 100% identifikationsgrad. Den föreslagna metoden identifierade också 48.9% av alla subnät som felkonfigurerade. Sammanfattning. För att sammanfatta så bevisar den föreslagna metoden att det är möjligt att identifiera felplacerade enheter på ett nätverk utifrån en analys av nätverkets metadata. Den föreslagna metoden är dessutom avsevärt snabbare än föregående metoder, men behöver utvecklas mer för att nå samma identifikationsgrad som föregående metoder. Det här arbetet kan ses som ett proof-of-concept gällande identifikation av enheter baserat på metadata, och behöver därför utvecklas för att nå sin fulla potential.
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Ekroth, Natalie, and Josefin Lennartsson. "Web-based Multicriteria Decision Analysis and Visualization for Reinvestments in Power Networks." Thesis, KTH, Geoinformatik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210696.
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It can be a hard and time consuming task for a decision maker to decide which parts of a network to reinvest in. There are a lot of parameters to take into consideration regarding reinvestments, for example age, number of outages, number of inspection remarks and the degree of inspection remarks. Without any visualization, it is difficult to detect patterns in the data. Therefor, the decision maker is required to really know the network he/she is working with and to have a gut feeling of where to reinvest. The purpose of this thesis is to show that the decision making process can be much simpler and better supported when using GIS tools for analysis and visualization. This is done by designing a prototype of a web application that can produce multicriteria decision analysis on the parameters of interest for reinvestments in a power network.Traditionally, heavy desktop clients are for expert users while web-based clients are better for layman users. One of the greatest advantages of a web-based client over a desktop client is that it can be reached externally from any device that has access to internet. Because of this, the prototype is developed as a web-based client. Customer data can be sensitive information, this means that the data needs to be secure and directly accessible for the users of the application. Therefor, a 3-tier architecture with client, server and database is used. The result is visualized in a map, which makes it easy for anyone to interpret the result. Since the prototype is developed to be used by none GIS experts, the weighted linear combination method is used for the analysis. The prototype is not fully automated and does not deliver an absolute decision, the goal is rather for it to function as an aid for the decision maker when deciding on the final reinvestment area. The prototype is evaluated by the prospective users of the application through a questionnaire and the results show that a tool like this would be very useful for reinvestments decisions. Since the prototype does not rely on topology or network structure, it can be adapted to other spatial decision problems than just reinvestments in power networks.
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Conley, Thomas A. "Effective Programmatic Analysis of Network Flow Data for Security and Visualization using Higher-order Statistics and Domain Specific Embedded Languages." Ohio University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1336482912.
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Park, Do young. "Robust Detection, Visualization, Recognition, and Analysis of Cytoskeletal Structures in Fibrillar Scaffolds from 3-Dimensional Confocal Images." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1500620844897981.
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Laurent, Anabelle. "The analysis of data from on-farm research network : Statistical approaches to test the efficacy of management practices and data visualization." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASB022.
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L’expérimentation en réseau d’agriculteurs permet de conduire des essais agronomiques dans des conditions réelles de production. L’intérêt est grandissant car cela représente une opportunité pour mettre en place des essais testant de nouveaux produits et de nouvelles pratiques agricoles dans les champs des agriculteurs. Généralement, les résultats sont présentés sous la forme de rapports individuels (i.e., un rapport résumant les résultats d’un essai en parcelle agricole), mais les informations fournies sont limitées, difficile à généraliser, et cela ne permet pas de synthétiser les résultats recueillis dans les différents essais. De plus, la variabilité de la réponse de rendement est inexplorée alors qu’elle constitue un potentiel pour améliorer la prise de décision par les agriculteurs. L’objectif général de cette thèse est de démontrer l’importance d’identifier des méthodes statistiques appropriées pour analyser et visualiser les données issues d’expérimentation en réseaux d’agriculteurs. Plus précisément, je me suis consacrée à l’analyse des réseaux gérés par Iowa Soybean Association, et d’un cas d’étude français. Un cadre d’analyse des données a été développé pour analyser simultanément plusieurs essais agricoles ayant un protocole expérimental commun et identifier les conditions dans lesquelles un traitement peut ou non être efficace. Ce cadre d’analyse utilise un modèle statistique à effets aléatoires par une approche bayésienne et fournit des estimations de réponse de rendement à l’échelle du réseau et des essais. Ce cadre d’analyse a été mis en application pour 51 pratiques agricoles sur le maïs et le soja au sein d’une application web. Celle-ci comprend des fonctions de visualisation dynamique et interactive des données afin d’améliorer le partage d’informations, et est accessible à un large public pour faciliter l’accès aux résultats issus des réseaux d’expérimentations. Un modèle statistique à effets aléatoires a été utilisé pour calculer des intervalles de prédiction décrivant la gamme des réponses de rendement plausibles pour un nouvel essai (en dehors de l’échantillon testé), et calculer la probabilité que la pratique agricole testée soit inefficace dans une nouvelle parcelle agricole. Selon le niveau de variabilité inter-essais, les intervalles de prédiction étaient 2,2 à 12,1 fois plus larges que les intervalles de confiance des réponses de rendement moyen (c'est-à-dire à l’échelle du réseau) pour toutes les pratiques agricoles testées. L'utilisation des intervalles de prévision et de la probabilité qu’un traitement soit inefficace empêcheront les agriculteurs d’avoir des attentes trop optimistes concernant le gain de rendement dans le cas où celui-ci est significatif à l’échelle du réseau. Le cadre d'analyse des données a été adapté à un réseau d’expérimentation français axé sur l'efficacité des produits de biocontrôle contre le Botrytis cinerea, le bicarbonate de potassium et Aureobasidium pullulans, sur la vigne en production biologique. Les résultats sont en faveur du bicarbonate de potassium car son efficacité sur l’incidence à l’échelle du réseau est plus élevée qu’Aureobasidium pullulans, pour une intensité de la maladie variant de 0 à 10%. Pour ces deux produits de biocontrôle, l'efficacité sur l'incidence pour un nouvel essai est très incertaine pour les niveaux d'intensité de maladie supérieurs à 15 %. Enfin, ce travail de thèse a permis d'étudier l'impact de l'échelle de la parcelle expérimentale (c'est-à-dire à essais en station expérimentale et expérimentation au champ) sur l'effet des pratiques agricoles sur le rendement et d'identifier la cause des écarts potentiels afin de mieux informer la prise de décision au niveau de l'exploitation et d'adapter l'extrapolation des résultats. Ce travail de thèse représente le premier effort majeur de consolidation des résultats d’expérimentation en réseaux d’agriculteurs et fournit des connaissances pour aider à la décision en termes de pratique agricoleAn on-farm research network is an organization of farmers that conducts agronomic experiments under local conditions. There is growing interest in on-farm research networks because they provide the infrastructure needed to test new products and management practices in farmers’ fields. Often, the results are usually presented as individual reports (i.e., a report summarizing the outcome for one trial), but this provides limited information difficult to generalize and does not allow presenting, in a synthetic way, all the results collected from the different trials. Moreover, there is an unexplored potential in detecting yield response variability patterns for better decision making. The overall objective of this thesis is to demonstrate the importance of identifying appropriate statistical methods for analyzing and visualizing on-farm research network data. Specifically, I focused on analyzing the on-farm research networks managed by the Iowa Soybean Association, and an adaptation was made with a French case-study. A data-analytics framework was developed to analyze multiple trials that use a common protocol and identify the conditions where an imposed treatment may or may not be effective. This framework used a random-effect model through a Bayesian approach and returned yield response estimates at the network and trial levels. The framework was implemented through a web-application for 51 different management practices on corn and soybean. The web-application includes dynamic data visualization features to enhance communication and information sharing, and is accessible to a broad audience to improve accessibility to on-farm research insights. A random-effects statistical model was used to compute prediction intervals describing a range of plausible yield response for a new (out-of-sample) trial, and compute the probability that the tested management practice will be ineffective in a new field. Depending on the level of between-trial variability, the prediction intervals were 2.2–12.1 times larger than confidence intervals for the estimated mean yield responses (i.e., at the network level) for all tested management practices. Using prediction intervals and the probability of ineffective treatment will prevent farmers from over-optimistic expectations that a significant effect at the network level will lead with high certainty to a yield gain on their farms. The data-analytic framework was adapted to a French on-farm research network focusing on the efficacy of biocontrol agent products against Botrytis cinerea, potassium bicarbonate and Aureobasidium pullulans, on organic vine. The results favored potassium bicarbonate as its efficacy on incidence at the network level is higher for diseased intensities between 0% and 10% than for Aureobasidium pullulans. For both biocontrol agents, the efficacy on incidence for a new trial is highly uncertain for intensity levels higher than 15%. Finally, this research investigated the impact of experimental plot scale (i.e., small-plot scale and field scale) on the effect of management practice on crop yield and identified the cause of potential discrepancies to inform on-farm decision-making better and adapt the extrapolation of the results. Taken together, this research represents the first major effort in consolidating results from on-farm research network and provides insight to make better farming management decisions
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Amento, Brian. "User Interfaces for Topic Management of Web Sites." Diss., Virginia Tech, 2001. http://hdl.handle.net/10919/29871.
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Topic management is the task of gathering, evaluating, organizing, and sharing a set of web sites for a specific topic. Current web tools do not provide adequate support for this task. We created and continue to develop the TopicShop system to address this need. TopicShop includes (1) a web crawler/analyzer that discovers relevant web sites and builds site profiles, and (2) user interfaces for information workspaces. We conducted an empirical pilot study comparing user performance with TopicShop vs. Yahooï . Results from this study were used to improve the design of TopicShop. A number of key design changes were incorporated into a second version of TopicShop based on results and user comments of the pilot study including (1) the tasks of evaluation and organization are treated as integral instead of separable, (2) spatial organization is important to users and must be well supported in the interface, and (3) distinct user and global datasets help users deal with the large quantity of information available on the web. A full empirical study using the second iteration of TopicShop covered more areas of the World Wide Web and validated results from the pilot study. Across the two studies, TopicShop subjects found over 80% more high-quality sites (where quality was determined by independent expert judgements) while browsing only 81% as many sites and completing their task in 89% of the time. The site profile data that TopicShop provide -- in particular, the number of pages on a site and the number of other sites that link to it -- were the key to these results, as users exploited them to identify the most promising sites quickly and easily. We also evaluated a number of link- and content-based algorithms using a dataset of web documents rated for quality by human topic experts. Link-based metrics did a good job of picking out high-quality items. Precision at 5 (the common information retrieval metric indicating the percentage of high quality items selected that are actually high quality) is about 0.75, and precision at 10 is about 0.55; this is in a dataset where 32% of all documents were of high quality. Surprisingly, a simple content-based metric, which ranked documents by the total number of pages on their containing site, performed nearly as well. These studies give insight into users' needs for the task of topic management, and provide empirical evidence of the effectiveness of task-specific interfaces (such as TopicShop) for managing topical collections.Ph. D.
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Perez, Sarah Isa Esther. "Exploring microbial community structure and resilience through visualization and analysis of microbial co-occurrence networks." Thesis, University of British Columbia, 2015. http://hdl.handle.net/2429/53928.
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Cultivation independent microbial ecology research relies on high throughput sequencing technologies and analytical methods to resolve the infinite diversity of microbial life on Earth. Microorganisms live in communities driven by genetic and metabolic processes as well as symbiotic relationships. Interconnected communities of microorganisms provide essential functions in natural and human engineered ecosystems. Modelling the community as an inter-connected system can give insight into the community's functional characteristics related to the biogeochemical processes it performs. Network science resolves associations between elements of structure to notions of function in a system and has been successfully applied to the study of microbial communities and other complex biological systems. Microbial co-occurrence networks are inferred from community composition data to resolve structural patterns related to ecological properties such as community resilience to disturbance and keystone species. However, the interpretation of global and local network properties from an ecological standpoint remains difficult due to the complexity of these systems creating a need for quantitative analytical methods and visualization techniques for co-occurrence networks. This thesis tackles the visualization and analytical challenges of modelling microbial community structure from a network science approach. First, Hive Panel Explorer, an interactive visualization tool, is developed to permit data driven exploration of topological and data association patterns in complex systems. The effectiveness of Hive Panel Explorer is validated by resolving known and novel patterns in a model biological network, the C. elegans connectome. Second, network structural robustness analysis methods are applied to study microbial communities from timber harvested forest soils from a North American longterm soil productivity study. Analyzing these geographically dispersed soils reveals biogeographic patterns of diversity and enables the discovery of conserved organizing principles shaping microbial community structure. The capacity of robustness analysis to identify key microbial community members as well as model shifts in community structure due to environmental change is demonstrated. Finally, this work provides insight into the relationship between microbes and their ecosystem, and characterizing this relationship can help us understand the organization of microbial communities, survey microbial diversity and harness its potential.Science, Faculty of
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Baur, Michael [Verfasser], and D. [Akademischer Betreuer] Wagner. "visone - Software for the Analysis and Visualization of Social Networks / Michael Baur. Betreuer: D. Wagner." Karlsruhe : KIT-Bibliothek, 2008. http://d-nb.info/1013721632/34.
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Zaidi, Faraz. "Analysis, structure and organization of complex networks." Thesis, Bordeaux 1, 2010. http://www.theses.fr/2010BOR14112/document.
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La Science des réseaux est apparue comme un domaine d'étude fondamental pour modéliser un grand nombre de systèmes synthétiques ou du monde réel.La découverte du graphe petit monde et du graphe sans échelle dans ces réseaux a révolutionné la façon d'étudier, d'analyser, de modéliser et de traiter ces réseaux. Dans cette thèse, nous nous intéressons à l'étude des réseaux ayant ces propriétés et souvent qualifiés de réseaux complexes.A notre avis, les recherches menées dans ce domaine peuvent être regroupées en quatre catégories: l'analyse, la structure, le processus/organisation et la visualisation.Nous abordons des problèmes relatifs à chacune de ces catégories tout au long de cette thèse. (...)Network science has emerged as a fundamental field of study to model many physicaland real world systems around us. The discovery of small world and scale free propertiesof these real world networks has revolutionized the way we study, analyze, model andprocess these networks. In this thesis, we are interested in the study of networks havingthese properties often termed as complex networks. In our opinion, research conducted inthis field can be grouped into four categories, Analysis, Structure, Processes-Organizationand Visualization. We address problems pertaining to each of these categories throughoutthis thesis. (...)
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Pister, Alexis. "Visual Analytics for Historical Social Networks : Traceability, Exploration, and Analysis." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG081.
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Cette thèse vise à identifier théoriquement et concrètement comment l'analyse visuelle peut aider les historiens dans leur processus d'analyse de réseaux sociaux. L'analyse de réseaux sociaux est une méthode utilisée en histoire sociale qui vise à étudier les relations sociales au sein de groupes d'acteurs (familles, institutions, entreprises, etc.) en reconstruisant les relations du passé à partir de documents historiques, tels que des actes de mariages, des actes de naissances, ou des recensements. L'utilisation de méthodes visuelles et analytiques leurs permet d'explorer la structure sociale formant ces groupes et de relier des mesures structurelles à des hypothèses sociologiques et des comportements individuels. Cependant, l'inspection, l'encodage et la modélisation des sources menant à un réseau finalisé donnent souvent lieu à des erreurs, des distorsions et des problèmes de traçabilité, et les systèmes de visualisation actuels présentent souvent des défauts d'utilisabilité et d'interprétabilité. En conséquence, les historiens ne sont pas toujours en mesure de faire des conclusions approfondies à partir de ces systèmes : beaucoup d'études se limitent à une description qualitative d'images de réseaux, surlignant la présence de motifs d'intérêts (cliques, îlots, ponts, etc.). Le but de cette thèse est donc de proposer des outils d'analyse visuelle adaptés aux historiens afin de leur permettre une meilleur intégration de leur processus global et des capacités d'analyse guidées. En collaboration avec des historiens, je formalise le processus d'une analyse de réseau historique, de l'acquisition des sources jusqu'à l'analyse finale, en posant comme critère que les outils utilisés dans ce processus devraient satisfaire des principes de traçabilité, de simplicité et de réalité documentaire (i.e., que les données présentées doivent être conformes aux sources) pour faciliter les va-et-vient entre les différentes étapes et la prise en main par l'utilisateur et ne pas distordre le contenu des sources. Pour satisfaire ces propriétés, je propose de modéliser les sources historiques en réseaux sociaux bipartis multivariés dynamiques avec rôles. Ce modèle intègre explicitement les documents historiques sous forme de nœuds, ce qui permet aux utilisateurs d'encoder, de corriger et d'analyser leurs données avec les mêmes outils. Je propose ensuite deux interfaces d'analyse visuelle permettant, avec une bonne utilisabilité et interprétabilité, de manipuler, d'explorer et d'analyser ce modèle de données. Le premier système ComBiNet offre une exploration visuelle de l'ensemble des dimensions du réseau à l'aide de vues coordonnées et d'un système de requêtes visuelles permettant d'isoler des individus ou des groupes et de comparer leurs structures topologiques et leurs propriétés. L'outil permet également de détecter les motifs inhabituels et ainsi de déceler les éventuelles erreurs dans les annotations. Le second système, PK-Clustering, est une proposition d'amélioration de l'utilisabilité et de l'efficacité des mécanismes de clustering dans les systèmes de visualisation de réseaux sociaux. L'interface permet de créer des regroupements pertinents à partir des connaissances a priori de l'utilisateur, du consensus algorithmique et de l'exploration du réseau dans un cadre d'initiative mixte. Les deux systèmes ont été conçus à partir des besoins et retours continus d'historiens, et visent à augmenter la traçabilité, la simplicité, et la réalité documentaire des sources dans le processus d'analyse de réseaux historiques. Je conclus sur la nécessité d'une meilleure intégration des systèmes d'analyse visuelle dans le processus de recherche des historiens. Cette intégration nécessite des outils plaçant les utilisateurs au centre du processus avec un accent sur la flexibilité et l'utilisabilité, limitant ainsi l'introduction de biais et les barrières d'utilisation des méthodes quantitatives, qui subsistent en histoireThis thesis aims at identifying theoretically and concretely how visual analytics can support historians in their social network analysis process. Historical social network analysis is a method to study social relationships between groups of actors (families, institutions, companies, etc.) through a reconstruction of relationships of the past from historical documents, such as marriage acts, migration forms, birth certificates, and censuses. The use of visualization and analytical methods lets social historians explore and describe the social structure shaping those groups while explaining sociological phenomena and individual behaviors through computed network measures. However, the inspection and encoding of the sources leading to a finalized network is intricate and often results in inconsistencies, errors, distortions, and traceability problems, and current visualization tools typically have usability and interpretability issues. For these reasons, social historians are not always able to make thorough historical conclusions: many studies consist of qualitative descriptions of network drawings highlighting the presence of motifs such as cliques, components, bridges, etc. The goal of this thesis is therefore to propose visual analytics tools integrated into the global social historians' workflow, with guided and easy-to-use analysis capabilities. From collaborations with historians, I formalize the workflow of historical network analysis starting at the acquisition of sources to the final visual analysis. By highlighting recurring pitfalls, I point out that tools supporting this process should satisfy traceability, simplicity, and document reality principles to ease bask and forth between the different steps, provide tools easy to manipulate, and not distort the content of sources with modifications and simplifications. To satisfy those properties, I propose to model historical sources into bipartite multivariate dynamic social networks with roles as they provide a good tradeoff of simplicity and expressiveness while modeling explicitly the documents, hence letting users encode, correct, and analyze their data with the same abstraction and tools. I then propose two interactive visual interfaces to manipulate, explore, and analyze this data model, with a focus on usability and interpretability. The first system ComBiNet allows an interactive exploration leveraging the structure, time, localization, and attributes of the data model with the help of coordinated views and a visual query system allowing users to isolate interesting groups and individuals, and compare their position, structures, and properties. It also lets them highlight erroneous and inconsistent annotations directly in the interface. The second system, PK-Clustering, is a concrete proposition to enhance the usability and effectiveness of clustering mechanisms in social network visual analytics systems. It consists in a mixed-initiative clustering interface that let social scientists create meaningful clusters with the help of their prior knowledge, algorithmic consensus, and interactive exploration of the network. Both systems have been designed with continuous feedback from social historians, and aim to increase the traceability, simplicity, and document reality of visual analytics supported historical social network research. I conclude with discussions on the potential merging of both tools, and more globally on research directions towards better integration of visual analytics systems on the whole workflow of social historians. Systems with a focus on those properties---traceability, simplicity, and document reality---can limit the introduction of bias while lowering the requirements for the use of quantitative methods for historians and social scientists which has always been a controversial discussion among practitioners
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Kaewprag, Pacharmon Fuhry. "Visual Analysis of Bayesian Networks for Electronic Health Records." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1531778349031686.
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Royer, Loic. "Unraveling the Structure and Assessing the Quality of Protein Interaction Networks with Power Graph Analysis." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-62562.
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Molecular biology has entered an era of systematic and automated experimentation. High-throughput techniques have moved biology from small-scale experiments focused on specific genes and proteins to genome and proteome-wide screens. One result of this endeavor is the compilation of complex networks of interacting proteins. Molecular biologists hope to understand life's complex molecular machines by studying these networks. This thesis addresses tree open problems centered upon their analysis and quality assessment.
First, we introduce power graph analysis as a novel approach to the representation and visualization of biological networks. Power graphs are a graph theoretic approach to lossless and compact representation of complex networks. It groups edges into cliques and bicliques, and nodes into a neighborhood hierarchy. We demonstrate power graph analysis on five examples, and show its advantages over traditional network representations. Moreover, we evaluate the algorithm performance on a benchmark, test the robustness of the algorithm to noise, and measure its empirical time complexity at O (e1.71)- sub-quadratic in the number of edges e.
Second, we tackle the difficult and controversial problem of data quality in protein interaction networks. We propose a novel measure for accuracy and completeness of genome-wide protein interaction networks based on network compressibility. We validate this new measure by i) verifying the detrimental effect of false positives and false negatives, ii) showing that gold standard networks are highly compressible, iii) showing that authors' choice of confidence thresholds is consistent with high network compressibility, iv) presenting evidence that compressibility is correlated with co-expression, co-localization and shared function, v) showing that complete and accurate networks of complex systems in other domains exhibit similar levels of compressibility than current high quality interactomes.
Third, we apply power graph analysis to networks derived from text-mining as well to gene expression microarray data. In particular, we present i) the network-based analysis of genome-wide expression profiles of the neuroectodermal conversion of mesenchymal stem cells. ii) the analysis of regulatory modules in a rare mitochondrial cytopathy: emph{Mitochondrial Encephalomyopathy, Lactic acidosis, and Stroke-like episodes} (MELAS), and iii) we investigate the biochemical causes behind the enhanced biocompatibility of tantalum compared with titanium.
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Leichtnam, Laetitia. "Detecting and visualizing anomalies in heterogeneous network events : Modeling events as graph structures and detecting communities and novelties with machine learning." Thesis, CentraleSupélec, 2020. http://www.theses.fr/2020CSUP0011.
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L'objectif général de cette thèse est d'évaluer l'intérêt des graphes dans le domaine de l'analyse des données de sécurité.Nous proposons une approche de bout en bout composé d'un modèle unifié de données réseau sous forme de graphes, d'un système de découverte de communauté, d'un système de détection d'anomalies non supervisé et d'une visualisation des données sous forme de graphes. Le modèle unifié est obtenue en utilisant des graphes de connaissance pour représenter des journaux d'évènements hétérogènes ainsi que du trafic réseau. La détection de communautés permet de sélectionner des sous-graphes représentant des événements fortement liés à une alerte ou à un IoC et qui sont donc pertinents pour l'analyse forensique. Notre système de détection d'intrusion basé sur les anomalies repose sur la détection de nouveauté par un autoencodeur et donne de très bons résultats sur les jeux de données CICIDS 2017 et 2018. Enfin, la visualisation immersive des données de sécurité permet de mettre en évidence les relations entre les éléments de sécurité et les événements malveillants ou les IoCs. Cela donne à l'analyste de sécurité un bon point de départ pour explorer les données et reconstruire des scénarii d'attaques globalesThe general objective of this thesis is to evaluate the interest of graph structures in the field of security data analysis.We propose an end-to-end approach consisting in a unified view of the network data in the form of graphs, a community discovery system, an unsupervised anomaly detection system, and a visualization of the data in the form of graphs. The unified view is obtained using knowledge graphs to represent heterogeneous log files and network traffics. Community detection allows us to select sub-graphs representing events that are strongly related to an alert or an IoC and that are thus relevant for forensic analysis. Our anomaly-based intrusion detection system relies on novelty detection by an autoencoder and exhibits very good results on CICIDS 2017 and 2018 datasets. Finally, an immersive visualization of security data allows highlighting the relations between security elements and malicious events or IOCs. This gives the security analyst a good starting point to explore the data and reconstruct global attack scenarii
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Dimanche, Vincent. "Compréhension fine du comportement des lignes des réseaux métro, RER ettramway pour la réalisation des études d’exploitabilité." Thesis, Reims, 2018. http://www.theses.fr/2018REIMS010.
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Les réseaux ferroviaires en milieu dense font face à des saturations importantes. Et l'adéquation entre l'offre théorique et la demande croissante impose des contraintes d'exploitabilités fortes. Un déséquilibre générera des points conflictuels comme des goulets d'étranglement avec pour effet des retards sur les trains amonts. Comme le facteur humain, parmi une multitude, influence l'exploitation ; le prendre en compte plus finement devrait améliorer la compréhension et la modélisation des lignes pour en accroître la capacité sans sacrifier le confort des passagers. Pour répondre à cet objectif, nos travaux reposent sur une visualisation adaptée des données remontées de l'exploitation et sur leur fouille automatisée. Elles ont été adaptées et appliquées au domaine ferroviaire notamment aux lignes des réseaux ferrés exploités par la RATP. Le processus « Visual Analytics », mis en œuvre dans nos travaux pour répondre à ces besoins, englobe les étapes nécessaires à la valorisation de la donnée, allant de leur préparation à l’analyse experte en passant par leur représentation graphique et par l’utilisation d'algorithmes de fouille de données. Parmi ces derniers, le CorEx et le Sieve nous ont permis par un apprentissage non supervisé basé sur une mesure de l'information mutuelle multivariée d'analyser les données d'exploitation pour en extraire des caractéristiques du comportement humain. Enfin, nous proposons aussi une visualisation intuitive d'une grande quantité de données permettant leur intégration et facilitant le diagnostic global du comportement des lignes ferroviairesDense railway networks face significant saturation. And the balance between the theoretical offer and the growing demand imposes strong operability constraints. An imbalance will generate conflicting points such as bottlenecks with the effect of delays on the following trains. As the human factor influences the operation performance; taking it into account more accurately should improve understanding and modeling of railway lines to increase capacity without reducing passenger comfort. To fulfill this objective, we are working on an adapted visualization of the operating data and on their automated mining. These two solutions have been adapted and applied to the railway sector, particularly to the lines of rail networks operated by RATP. The "Visual Analytics" process, implemented in our work to meet these needs, encompasses the steps required to value the data, going from the preparation of the data to the expert analysis. This expert analysis is made through graphic representation and the use of data mining algorithms. Among these data mining algorithms, CorEx and Sieve allowed us to analyze operating data and then extract characteristics human behavior thanks to unsupervised learning based on a multivariate mutual information measure to. Finally, we propose an intuitive visualization of a large amount of data allowing their global integration and facilitating the overall diagnosis of the railway lines behavior
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Nascimento, Cátia Souza do. "PANDORA : uma ferramenta para visualização incremental e análise de redes sociais acadêmicas." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2013. http://hdl.handle.net/10183/67851.
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A análise de redes sociais através de ferramentas visuais permite ao usuário extrair informações importantes sobre os indivíduos e seus relacionamentos. Através dela é possível entender de que forma os grupos se organizam. Para o caso de redes de coautoria, algumas conclusões como qual o pesquisador com maior prestígio na rede ou qual pesquisador tem o perfil mais parecido com o seu podem ser obtidas. Uma grande quantidade de ferramentas têm sido desenvolvidas para visualização de redes sociais. Algumas delas permitem que análises sejam feitas sobre os dados que compõem as redes, porém a maioria não mostra os resultados dessas análises sobre o próprio grafo, em geral apresentando-as como informações textuais. Neste trabalho, inicialmente, foi feito um estudo comparativo de diversas ferramentas visuais que além de mostrar redes sociais de forma gráfica, fazem algum tipo de análise sobre a rede. O estudo subsidiou o desenvolvimento de Pandora, uma ferramenta que permite visualização incremental de redes acadêmicas além de interação com o grafo e cálculo de algumas métricas, como centralidades, assortatividade e coeficiente de colaboração.The analysis of social networks through visual tools allows to extract important information about each individual and their relationships. Through it we can understand how groups are organized. For the case of co-authorship networks, some conclusions about which researcher has greater prestige in the network or where the researcher has profile more like his can be obtained. A lot of tools have been developed for visualizing social networks. Some of them allow analyzes are made about the data that make up the networks, but most do not show the results of these analyzes on the graph itself, usually presenting them as textual information. This work was initially done a comparative study of various visual tools that show beyond social networks graphically. This tools generally do some kind of analysis on the network. In this work Pandora was developed, a visualization tool that allows incremental academic networks and interaction with the graph and calculate some metrics, such as centralities, assortativity and collaboration coefficient.
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Janowski, Sebastian Jan [Verfasser]. "VANESA - A bioinformatics software application for the modeling, visualization, analysis, and simulation of biological networks in systems biology applications / Sebastian Jan Janowski." Bielefeld : Universitaetsbibliothek Bielefeld, 2013. http://d-nb.info/1036112020/34.
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Fuhry, David P. "PLASMA-HD: Probing the LAttice Structure and MAkeup of High-dimensional Data." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1440431146.
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Jusufi, Ilir. "Multivariate Networks : Visualization and Interaction Techniques." Doctoral thesis, Linnéuniversitetet, Institutionen för datavetenskap (DV), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-25497.
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As more and more data is created each day, researchers from different science domains are trying to make sense of it. A lot of this data, for example our connections to friends on different social networking websites, can be modeled as graphs, where the nodes are actors and the edges are relationships between them. Researchers analyze this data to find new forms of communication, to explore different social groups or subgroups, to detect illegal activities or to seek for different communication patterns that could help companies in their marketing campaigns. Another example are huge networks in system biology. Their visualization is crucial for the understanding of living beings. The topological structure of a network on its own could give insight into the existence or distribution of interesting actors in the network. However, this is often not enough to understand complex network systems in real-world applications. The reason for this is that all the network elements (nodes or edges) are not simple one-dimensional data. For instance in biology, experiments can be performed on biological networks. These experiments and network analysis approaches produce additional data that are often important to be analyzed with respect to the underlying network structure. Therefore, it is crucial to visualize the additional attributes of the network while preserving the network structure as much as possible. The problem is not trivial as these so-called multivariate networks could have a high number of attributes that are related to their nodes, edges, different groups, or clusters of nodes and/or edges. The aim of this thesis is to contribute to the development of different visualization and interaction techniques for the visual analysis of multivariate networks. Two research goals are defined in this thesis: first, a deeper understanding of existing approaches for visualizing multivariate networks should be acquired in order to classify them into categories and to identify disadvantages or unsolved visualization challenges. The second goal is to develop visualization and interaction techniques that will overcome various issues of these approaches. Initially, a brief survey on techniques to visualize multivariate networks is presented in this thesis. Afterwards, a small task-based user study investigating the usefulness of two main approaches for multivariate network visualization is discussed. Then, various visualization and interaction techniques for multivariate network visualization are presented. Three different software tools were implemented to demonstrate our research efforts. All features of our systems are highlighted, including a description of visualization and interaction techniques as well as disadvantages and scalability issues if present.
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Ribeiro, Elvia Nunes. "ANÁLISE E VISUALIZAÇÃO DAS REDES SOCIAIS, USANDO O SOFTWARE R, APLICADA À EDUCAÇÃO A DISTÂNCIA." Pontifícia Universidade Católica de Goiás, 2017. http://tede2.pucgoias.edu.br:8080/handle/tede/3694.
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This research applies to the social network analysis metrics (SNA), information visualization (VI) and correlation analysis for development of diagnostics of the distance learning on social networks that occur in the virtual learning environment (AVA). The data were extracted directly from the database of the AVA, e-Proinfo. The use of R software version 3.3.1 was chosen for the implementation of the necessary scripts in this research. The R software proved to be a suitable and versatile choice for applications in networks, contemplating the options generally available in SNA specific tools and resources for handling, processing and implementation of new network solutions. The analysis of forum networks identified the intensity of individual and class participation. Non-parametric analysis applied found a high positive correlation between the grade of the student and his contributions to the forum, especially the centrality of output. The correlations of Spearman and Kendall were 0.7921 and 0.6261 respectively. The analysis of social networks, messaging and note tools and the establishment of contacts among the participants turned possible the identification of the level of their involvement in the course. This research contributes to knowledge management by highlighting social networks formed by communications data and course interactions. Access to social networks of a distance course enables the Manager to monitor the level of participation, exchanges of information and the construction of knowledge. This information is useful for the decision-making and can collaborate for educational development.
Esta pesquisa aplica as métricas de análise de redes sociais (ARS), visualização de informações (VI) e análise de correlação para realização de diagnósticos de um curso a distância, nas redes sociais que ocorrem no ambiente virtual de aprendizagem (AVA). Os dados foram extraídos diretamente do banco de dados do AVA, e-Proinfo. Optou-se pelo uso do software R, versão 3.3.1, para a implementação dos scripts necessários nesta pesquisa. O software R mostrou ser uma escolha adequada e versátil para aplicações em redes, contemplando as opções geralmente disponíveis nas ferramentas específicas de ARS e recursos para manipulação, tratamento e implementação de novas soluções de redes. As análises das redes de fórum permitiram identificar a intensidade das participações individuais e das turmas. As análises não paramétricas aplicadas constataram uma alta correlação positiva entre a nota do aluno e suas contribuições no fórum, principalmente, a centralidade de saída. As correlações de Spearman e Kendal foram de 0,7921 e 0,6261 respectivamente. As análises das redes sociais, das ferramentas de mensagem e de recados e o estabelecimento de contatos entre os participantes permitiram identificar o nível de envolvimento destes no curso. Esta pesquisa contribui com a gestão de conhecimento por evidenciar as redes sociais formadas pelos dados de comunicações e de interações do curso. O acesso às redes sociais de um curso a distância possibilita ao gestor acompanhar o nível de participação, as trocas de informações e a construção de conhecimento. Estas informações são úteis para as tomadas de decisões e podem colaborar para o desenvolvimento educacional.
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Ling, Kwan Lai. "Visualization of ATM network data." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ28956.pdf.
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Sun, Wenyi, and Chunmiao Yu. "Visualization of Lnu's Publication Network." Thesis, Linnéuniversitetet, Institutionen för datavetenskap, fysik och matematik, DFM, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-13893.
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DiVA, Academic Archive On-line, is a website which can provide the information from the most academic publications of Swedish Universities. The information includes the title, author, publication data, and so on. The aim of this project is to design a tool to visualize the co-authorship publication network and be able to transform the data into a more readable form. For parsing and visualizing data, the tool adopts the “InforVis Reference” model. Here a set of interaction and visualization technologies are adopted, so that it can create view base on user’s query with increasing the usability. In this thesis, we present a “use case” by applying our tool to visualize a part of articles published at Linnaeus University and to illustrate the capability and functions of the tool. The tool can also provide user a comfortable operating environment with a quick searching speed and high efficiency; it can be used anywhere, if internet is available.
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Ren, Haolin. "Visualizing media with interactive multiplex networks." Thesis, Bordeaux, 2019. http://www.theses.fr/2019BORD0036/document.
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Les flux d’information suivent aujourd’hui des chemins complexes: la propagation des informations, impliquant éditeurs on-line, chaînes d’information en continu et réseaux sociaux, emprunte alors des chemins croisés, susceptibles d’agir sur le contenu et sa perception. Ce projet de thèse étudie l’adaptation des mesures de graphes classiques aux graphes multiplexes en relation avec le domaine étudié, propose de construire des visualisations à partir de plusieurs représentations graphiques des réseaux, et de les combiner (visualisations multi-vues synchronisées, représentations hybrides, etc.). L’accent est mis sur les modes d’interaction permettant de prendre en compte l’aspect multiplexe (multicouche) des réseaux. Ces représentations et manipulations interactives s’appuient aussi sur le calcul d’indicateurs propres aux réseaux multiplexes. Ce travail est basé sur deux jeux de données principaux: l’un est une archive de 12 ans de l’émission japonaise publique quotidienne NHK News 7, de 2001 à 2013. L’autre recense les participants aux émissions de télévision/radio françaises entre 2010 et 2015. Deux systèmes de visualisation s’appuyant sur une interface Web ont été développés pour analyser des réseaux multiplexes, que nous appelons «Visual Cloud» et «Laputa». Dans le Visual Cloud, nous définissons formellement une notion de similitude entre les concepts et les groupes de concepts que nous nommons possibilité de co-occurrence (CP). Conformément à cette définition, nous proposons un algorithme de classification hiérarchique. Nous regroupons les couches dans le réseau multiplexe de documents, et intégrons cette hiérarchie dans un nuage de mots interactif. Nous améliorons les algorithmes traditionnels de disposition de mise en forme de nuages de mots de sorte à préserver les contraintes sur la hiérarchie de concepts. Le système Laputa est destiné à l’analyse complexe de réseaux temporels denses et multidimensionnels. Pour ce faire, il associe un graphe à une segmentation. La segmentation par communauté, par attribut, ou encore par tranche temporelle, forme des vues de ce graphe. Afin d’associer ces vues avec le tout global, nous utilisons des diagrammes de Sankey pour révéler l’évolution des communautés (diagrammes que nous avons augmentés avec un zoom sémantique). Cette thèse nous permet ainsi de parcourir trois aspects (3V) des plus intéressants de la donnée et du BigData appliqués aux archives multimédia: Le Volume de nos données dans l’immensité des archives, nous atteignons des ordres de grandeurs qui ne sont pas praticables pour la visualisation et l’exploitation des liens. La Vélocité à cause de la nature temporelle de nos données (par définition). La Variété qui est un corollaire de la richesse des données multimédia et de tout ce que l’on peut souhaiter vouloir y investiguer. Ce que l’on peut retenir de cette thèse c’est que la traduction de ces trois défis a pris dans tous les cas une réponse sous la forme d’une analyse de réseaux multiplexes. Nous retrouvons toujours ces structures au coeur de notre travail, que ce soit de manière plus discrète dans les critères pour filtrer les arêtes par l’algorithme Simmelian backbone, que ce soit par la superposition de tranches temporelles, ou bien que ce soit beaucoup plus directement dans la combinaison d’indices sémantiques visuels et textuels pour laquelle nous extrayons les hiérarchies permettant notre visualisationNowadays, information follows complex paths: information propagation involving on-line editors, 24-hour news providers and social medias following entangled paths acting on information content and perception. This thesis studies the adaptation of classical graph measurements to multiplex graphs, to build visualizations from several graphical representations of the networks, and to combine them (synchronized multi-view visualizations, hybrid representations, etc.). Emphasis is placed on the modes of interaction allowing to take in hand the multiplex nature (multilayer) of the networks. These representations and interactive manipulations are also based on the calculation of indicators specific to multiplex networks. The work is based on two main datasets: one is a 12-year archive of the Japanese public daily broadcast NHK News 7, from 2001 to 2013. Another lists the participants in the French TV/radio shows between 2010 and 2015. Two visualization systems based on a Web interface have been developed for multiplex network analysis, which we call "Visual Cloud" and "Laputa". In the Visual Cloud, we formally define a notion of similarity between concepts and groups of concepts that we call co-occurrence possibility (CP). According to this definition, we propose a hierarchical classification algorithm. We aggregate the layers in a multiplex network of documents, and integrate that hierarchy into an interactive word cloud. Here we improve the traditional word cloud layout algorithms so as to preserve the constraints on the concept hierarchy. The Laputa system is intended for the complex analysis of dense and multidimensional temporal networks. To do this, it associates a graph with a segmentation. The segmentation by communities, by attributes, or by time slices, forms views of this graph. In order to associate these views with the global whole, we use Sankey diagrams to reveal the evolution of the communities (diagrams that we have increased with a semantic zoom). This thesis allows us to browse three aspects of the most interesting aspects of the data miming and BigData applied to multimedia archives: The Volume since our archives are immense and reach orders of magnitude that are usually not practicable for the visualization; Velocity, because of the temporal nature of our data (by definition). The Variety that is a corollary of the richness of multimedia data and of all that one may wish to want to investigate. What we can retain from this thesis is that we met each of these three challenges by taking an answer in the form of a multiplex network analysis. These structures are always at the heart of our work, whether in the criteria for filtering edges using the Simmelian backbone algorithm, or in the superposition of time slices in the complex networks, or much more directly in the combinations of visual and textual semantic indices for which we extract hierarchies allowing our visualization
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Jin, Zhihua. "Visualization of Network Traffic to Detect Malicious Network Activity." Thesis, Norwegian University of Science and Technology, Department of Telematics, 2008. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-8951.
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Today, enormous logging data monitoring the traffics of the Internet is generated everyday. However, the network administrators still have very limited insight into the logging data, mainly due to the lack of efficient analyzing approaches. Most of the existing network monitoring or analysis tools either mainly focus on the throughput of the network in order to assist network structure planning and optimization, which is too high level for security analysis, or dig to too low level into every packet, which is too inefficient in practice. Unfortunately, not all network traffics are legitimate. As a matter of fact, a lot of malicious traffics flow through the Internet all the time. Such malicious traffics can lead to various cyber-crimes, and exhaust considerable network bandwidth. The expression that what you do not see can hurt you perfectly suits the situation here. In order to help the network administrators to discover malicious activities in their network traffics, this thesis attempt to explore suitable visualization techniques to distinguish malicious traffics from massive background traffics by using visual patterns, to which the human visual perception system is sensitive and can thus processes efficiently. To achieve such goal, we first extract the visual patterns of malicious activities from known malicious traffics. Then, we look for the same visual patterns in the normal traffics. When the same visual pattern is found, we identify the relevant malicious activities. The tool used in our experimentation is designed and implemented according to the experiences learned from previous related works, with special regards to human visual perception theory. The result of our experimentation shows that some malicious activities which can not be easily identified in traditional analyzing approaches before, can be identified by our visualization system under certain conditions.
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Sledz, Daniel A. Coomes Donald E. "A dynamic three-dimensional network visualization program for integration into cyberciege and other network visualization scenarios." Monterey, Calif. : Naval Postgraduate School, 2007. http://bosun.nps.edu/uhtbin/hyperion-image.exe/07Jun%5FSledz.pdf.
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Thesis (M.S. in Modeling, Virtual Environments, and Simulation (Moves))--Naval Postgraduate School, June 2007.Thesis Advisor(s): Mathias Kölsch. "June 2007." Includes bibliographical references (p. 113-116). Also available in print.
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Sledz, Daniel A. Coomes Donald E. "A dynamic three-dimensional network visualization program for integration into cybersiege and other network visualization scenarios." Monterey, Calif. : Naval Postgraduate School, 2007. http://handle.dtic.mil/100.2/ADA470113.
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Thesis (M.S. in Modeling, Virtual Environments, and Simulation (Moves))--Naval Postgraduate School, June 2007."June 2007." Title from title page of PDF document (viewed on: Nov. 15, 2007). Includes bibliographical references (p. 113-116).