In an era of increasingly complex data, the balance between detailed information presentation and maintaining an overview during visualisation is crucial. This thesis investigates the application of the ´magic lens' technique to network visualisations, specifically focusing on visualising dynamic network attributes such as temporal node changes and evolving network topology. Traditional network visualisation methods often struggle to reveal underlying multivariate characteristics, necessitating more innovative approaches like focus plus context visualisations. The lens is also to be used for comparison between different parts of the network, where multivariate node attributes and dynamic network topology is integrated. Our research aims to enhance the usability and effectiveness of network visualisations by integrating a magic lens for dynamic data exploration. This study re-implements and extends the Network Lens tool using modern web technologies (Cytoscape.js and D3.js) and conducts a user study to evaluate its design and functionality. The research questions address essential design requirements for a comparison lens, methods for integrating temporal network comparisons within the magic lens, and user preference for different visual representations. Through careful study and implementation, we identify key design requirements for effective comparison lenses, including intuitive node selection, multi-selection comparison, and longitudinal observation capabilities. The dynamic network topology was visualised using a timeline representation, positioned adjacent to the main lens to maintain clarity and ease of use. User evaluations highlighted the practicality of dual-lens setups for side-by-side comparison, although initial usability of the dynamic display suggests room for refinement. Three visual representations - bar charts, parallel coordinates plots, and star plots - were integrated within the magic lens to enhance multivariate data exploration. User feedback indicated a preference for bar charts due to their straightforward interpretation, despite their limitations in displaying small relative values. Parallel coordinates plot were favoured at second place for their ability to maintain visual clarity across a range of values, while star plots, though less preferred, were recognised for their potential in displaying numerous dimensions. Future work should extend the tool's application to real-world datasets, incorporate dynamic nominal data, and explore alternative dynamic display methods. Broader user studies across various domains will further validate andrefine the tool, ensuring its effectiveness and adaptability for diverse data visualisation needs. / I en tid av allt mer komplex data är balansen mellan detaljerad informationspresentation och upprätthållande av överblick under visualisering avgörande. Detta projekt undersöker tillämpningen av den "magiska linsen"-tekniken för nätverksvisualiseringar, speciellt med fokus på visualisering av dynamiska nätverksattribut såsom temporala nodförändringar och förändrande nätverkstopologi. Traditionella nätverksvisualiseringsmetoder har ofta besvär för att avslöja underliggande multivariata egenskaper, vilket kräver mer innovativa tillvägagångssätt som fokus och kontextvisualiseringar. Linsen ska också användas för jämförelse mellan olika delar av nätverket, där multivariata nodattribut och dynamisk nätverkstopologi är integrerade. Vår forskning syftar till att förbättra användbarheten och effektiviteten av nätverksvisualiseringar genom att integrera en magisk lins för dynamisk datautforskning. Den här studien omimplementerar och utökar verktyget Network Lens med modern webbteknologi (Cytoscape.js och D3.js) och genomför en användarstudie för att utvärdera dess design och funktionalitet. Forskningsfrågorna tar upp väsentliga designkrav för en jämförelselins, metoder för att integrera tidsmässiga nätverksjämförelser inom den magiska linsen och användarpreferenser för olika visuella representationer. Genom noggranna studier och implementering identifierar vi nyckeldesignkrav för effektiva jämförelselinser, inklusive intuitivt nodval, jämförelse med flera val och longitudinella observationsmöjligheter. Den dynamiska nätverkstopologin visualiserades med hjälp av en tidslinjerepresentation, placerad intill huvudlinsen för att bibehålla klarhet och användarvänlighet. Användarutvärderingar lyfte fram det praktiska i funktionen med dubbla linser för jämförelse sida vid sida, även om den initiala användbarheten av den dynamiska displayen antyder utrymme för förfining. Tre visuella representationer - stapeldiagram, parallella koordinater och stjärndiagram - integrerades i den magiska linsen för att förbättra multivariat datautforskning. Användarfeedback indikerade en preferens för stapeldiagram på grund av deras enkla tolkning, trots deras begränsningar i att visa små relativa värden. Parallella koordinater gynnades på andra plats för dens förmåga att bibehålla visuell klarhet över en rad värden, medan stjärndiagram, även om de var mindre föredragna, erkändes för sin potential i att visa många dimensioner. Framtida arbete bör utvidga verktygets tillämpning till datauppsättningar i verkliga världen, inkludera dynamiska nominella data och utforska alternativa dynamiska visningsmetoder. Bredare användarstudier över olika domäner kommer att ytterligare validera och förfina verktyget, vilket säkerställer dess effektivitet och anpassningsförmåga för olika datavisualiseringsbehov.

As the digital age progresses, more and more data and information is produced. At the same time, platforms and services have become increasingly more interconnected. Network visualization can best represent and understand this data and this interconnectedness. Networks and graphs are fairly flexible and allow us to better understand e.g. who are friends with whom on Facebook, what neurotransmitter is communicating with what and with which chemical, and so on. Being able to capture complex emerging patterns and broad-perspective information is best done with clear and concise graphs. A survey on recent research was conducted to investigate these trends, and the methodology followed previous work. The trends we find paint a mixed picture; while reporting of fundamental information is still lacking, we see a new interest in cognition and a new exciting direction in which graph visualization is going.

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.

Modern vehicles equipped with many hardware and software systems. To develop new functionalities and maintain existing features of the vehicles, engineers have to check relationship between software and hardware systems. Due to complexity of these relationships, visual representation of how systems are working together is required to make R&D process easier. At Scania, engineers using node-link diagrams to represent logical function architecture of the Scania vehicles. Logical function architecture is a part of electrical and electronic systems in the automotive industry, due to size of these systems they are considered as a large network. Visualizing large networks by diagrams is not a new problem in literature. At past, researches published about diagram drawing and algorithms have been developed to generate good looking diagram. However, sometimes due to complexity of the data, having complex and unreadable diagrams are unavoidable and they are hard to understand. Previous studies investigated how diagrams should be drawn, however focus was not how users should interact with the diagrams. In node-link diagrams users follows edges to understand relationships between components. Having edges in the diagram heavily affects the diagram drawing time and also required space for the diagram. In this paper I developed an artefact which is not using edges to visualize LFA at Scania. Artefact usability has been tested with Scania engineers by giving some tasks to them. In the tests, artefact without edges achieved better results than node-link diagram and 426% improvement achieved by comparing task completion times in seconds. The artefact proved that it can be powerful alternative to classic node-link diagram visualization.

Visualization of complex data is a challenging topic. Data are often stored in spreadsheets making it difficult to get an overview of otherwise inaccessible information. Visualization of data is necessary for getting an understanding of complex structures. Organizations, among them financial institutions, nowadays consist of large owner structures and legal structures. Visualization of these structures is a challenging task due to the many levels of complexity within these structures.This report presents a visualization prototype of the legal business structures of financial institutions. The primary function of this prototype is to facilitate the understanding of complex legal business structures that would be hard to comprehend only from spreadsheets. The development of the prototype was performed using Python and NetworkX and the visualization was constructed as a graph representation. The evaluation of the prototype was conduced with semi-structured interviews together with a demonstration. The evaluation indicated that the utility of the visualization prototype concept can be further improved. The results suggests that a prototype is vital and is of good use for facilitating understanding of data. / Visualisering av data är ett svårt problem. Diverse data lagras ofta i textform vilket bidrar till en sämre översikt av datan. Med hjälp av visualisering kan man få en bättre förståelse för komplexa strukturer i datan. Organisationer så som finansinstitut involverar ofta stora ägarstrukturer och legala strukturer. Att kunna visualisera dessa strukturer blir då ett problem på grund av deras komplexitet.I denna rapport presenteras en visualiseringsprototyp av legala affärsstrukturer hos finansinstitut. Huvudsyftet med denna prototyp är att få en bättre förståelse av strukturer som annars är svåra att analysera utifrån enbart kalkylblad. Prototypen implementerades med hjälp av Python och NetworkX och visades visuellt som en graf representation. Evalueringen utfördes med hjälp av intervjuer samt en demonstration av prototypen. Evalueringen visar på att användarna ser en nytta med prototypen vilket tyder på att det finns utrymme för att vidare utveckla konceptet. Resultatet antyder att en visualisering är väsentlig när det kommer till att underlätta analys av data.

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