78 research outputs found
AdMaTilE: Visualizing Event-Based Adjacency Matrices in a Multiple-Coordinated-Views System (Poster Abstract)
No full textConventional dynamic networks represent network changes via a discrete sequence of timeslices, which usually entails loss of information on fine-grained dynamics. Recently, event-based networks emerged as an approach to model this temporal (event-based) information more precisely. Adjacency-matrix-based visualizations of temporal networks are under-investigated in related literature and present a promising research direction for network visualization. Our approach AdMaTilE (Adjacency Matrix and Timeline Explorer) is designed to visualize event-based networks using multiple matrix views, timelines, difference maps, and staged transitions
EuroVis Workshop on Visualization Play, Games, and Activitie 2025: Frontmatter
No full textEuroVis Workshop on Visualization Play, Games, and Activitie
Exiled but not forgotten: Investigating commemoration of musicians in Vienna after 1945 through Visual Analytics
No full text[Preprint] Paper presented at Biographical Data in a Digital World (2019), Varna, Bulgari
Interactive Music Mapping Vienna: Networks In Time and Space
Full text linkIn the application domain of digital humanities network visualization is increasingly being used to conduct research as the main interests of the domain experts lie in exploring and analyzing relationships between entities and their changes over time. Visualizing the dynamics and different perspectives of such data is a non-trivial task but it enables researchers to explore connections between disparate entities and investigate historical narratives that emerge. We present our work on an interactive exploration environment to visualize event-based networks and support research in digital humanities through visualization of historical subjects in space and time. Our approach is a circular visualization that is flexible and transparent to the underlying subject. It uses event-based networks as inputs, considering the existence of a small set of entities in space and time: people, places, events, and themes. This minimal model is powerful enough to represent many facets of historical subjects and explore trends and narratives mainly through their temporal and spatial developments, serving as visual analytical support to humanists
Netzwerke in Zeit und Raum : visuelle Analyse dynamischer Netzwerkdarstellungen
Full text linkNetworks are abstract and flexible data structures that model entities and relationships between them. Network visualization encompasses techniques, designed to produce aesthetic and scalable layouts of such data. In real-world applications data changes over time and investigating and analyzing these dynamics is of interest, motivating the study of dynamic network visualization. Dynamic network visualization explores how networks evolve and how these changes can be effectively conveyed. The goal is to construct representations depicting both structural and temporal information in a readable manner. This is a challenging task and current approaches often optimize standard network representations (i.e., node-link diagrams) to simultaneously portray topology and dynamics using animation. Alternative visualization modalities are seldom explored, presenting an interesting research direction. As network connectivity can be cumbersome to visualize, approaches make use of space to improve readability and implement aesthetic criteria, often presenting scalability concerns. These limitations pose interesting research questions that are investigated in this dissertation. Namely, how can we augment standard network visualization techniques to convey dynamics, which techniques are appropriate, effective, and avoid information overload, and are alternative visualization metaphors for dynamic networks useful? We contribute to dynamic network visualization by formally evaluating the design space and investigating novel dynamic network visualization approaches. Our results show promise for alternative visualization modalities and our evaluation of structural and temporal encodings highlights the usefulness of under-investigated combinations in related literature. Furthermore, we outline research challenges that present future work opportunities and provide recommendations to support researchers in developing dynamic network visualization approaches
Visual exploration and comparison of multiple resume : focus on time and space
Full text linkDaten, die in Lebensläufen vorhanden sind, haben sowohl zeitliche als auch räumliche Dimensionen, wie etwa die Berufserfahrung oder Bildung einer Person. Diese Information ist umfassend - es ist sehr klar, wann, wo und wie lange eine Person gearbeitet oder studiert hat. Es wird immer schwieriger, einen klaren Überblick zu erhalten, wenn man mehrere Lebensläufe, insbesondere ihre chronologischen Informationen, vergleichen will. Das Umschalten zwischen den verschiedenen Lebensläufen um jedes einzelne Ereignis zu vergleichen, ist sehr ineffizient. Die häufigste und intuitivste Art, Ereignisse aus mehreren Lebensläufen zu vergleichen, wäre, sie in einem Seite-an-Seite Modus zu sehen. Viele Ähnlichkeiten können aus diesem Modus schnell erkannt werden, aber in den Fällen, wo mehr als zwei Lebensläufe gleichzeitig verglichen werden müssen, verliert man den Überblick. Die Menge an Informationen steigt erheblich und der Vergleich von Ereignissen wird zu einer schwierigen Aufgabe. Wir schlagen das Design und die Implementierung einer Webapplikation vor - CV3, die die Möglichkeit anbietet, mehrere Chronologien gleichzeitig zu vergleichen und die Ergebnisse klar visualisieren kann. Dabei ist unsere Ziel einen sauberen Überblick der Ähnlichkeiten bzw. Unterschiede der verschiedenen Lebensläufen zu erhalten. Unser Ansatz unterstützt Benutzer_innen bei der Filterung von chronologischen Ereignissen über alle Lebensläufe hinweg, erkennt und extrahiert relevante Informationen aus Lebensläufen und visualisiert ihre Ähnlichkeiten effizient in einer einzigen klaren Übersicht.Information present in resumes has temporal as well as spatial dimensions, such as a person¿s work experience or education. This information by itself is comprehensible - it is very clear when, where, and for how long a person has worked or studied. It becomes increasingly difficult to maintain a clear overview when comparing multiple resumes, specifically their chronological information. Navigating back and forth between resumes, switching between views and attempting to compare every single event is highly inefficient. The most common and intuitive way to compare events from multiple resumes, would be to view them in a side-by-side fashion. Many similarities can be quickly recognized by doing this, but in cases where more than two resumes need to be compared simultaneously, the overview becomes cluttered, the amount of information increases substantially and comparing events becomes a difficult task. We propose the design and implementation of a web application - CV3 - that is capable of comparing multiple chronologies and visualizing the output in a clear manner, whilst maintaining a clean overview. Our approach supports users in filtering timeline events across all resumes, recognizing and extracting relevant information from resumes and visualizing their similarities efficiently in a single overview
Blockchain Forensics: A Modern Approach to Investigating Cybercrimes in the Age of Decentralisation
No full textBattleGraphs: Forge, Fortify, and Fight in the Network Arena
No full textConstructive visualization enables users to create personalized data representations and facilitates early insight generation and sensemaking. Based on NODKANT, a toolkit for creating physical network diagrams using 3D printed parts, we define a competitive network physicalization game: BattleGraphs. In BattleGraphs, two players construct networks independently and
compete in solving network analysis benchmark tasks. We propose a workshop scenario where we deploy our game, collect strategies for interaction and analysis from our players, and measure the effectiveness of the strategy with the success of the player to discuss in a reflection phase. Printable parts of the game, as well as instructions, are available through the Open Science Framework at -- https://osf.io/x6zv7/ -- All proceedings (including this submission) available on the eurographics digital library: https://diglib.eg.org/collections/d1483cdb-603e-46b6-b315-d9a6e750427
Dynamic Perspectives: Visualizing Time and Networks for Analytical Insights
Full text linkDynamic networks are structures where the graph's nodes and/or edges can appear or disappear over discrete or continuous intervals of time. The visualization and analysis of dynamic networks play an essential role in understanding the structural evolution of a network. The main goals are to support a better overview of the network's evolution and to identify patterns or behaviors. Dynamic networks are most commonly represented as node-link diagrams with the temporal dimension being depicted using animated approaches.
In this talk, I present a number of different and alternative methods for visualizing and interacting with a network and its temporal dimension and discuss the most pressing challenges currently faced. Furthermore, possible intersections with the Materials Sciences domain are discussed to understand how dynamic network visualization can complement the current state-of-the-art in this domain, provide different perspectives, and facilitate new insights into the data
On Time and Space: An Experimental Study on Graph Structural and Temporal Encodings
No full textDynamic networks reflect temporal changes occurring to the graph’s structure and are used to model a wide variety of problems
in many application fields. We investigate the design space of dynamic graph visualization along two major dimensions: the network structural and temporal representation. Significant research has been conducted evaluating the benefits and drawbacks of different structural representations for static graphs, however, few extend this comparison to a dynamic network setting. We conduct a study where we assess the participants’ response times, accuracy, and preferences for different combinations of the graph’s structural and temporal representations on typical dynamic network exploration tasks, with and without support of common interaction methods. Our results suggest that matrices provide better support for tasks on lower-level entities and basic interactions require longer response times while increasing accuracy. Node-link with auto animation proved to be the quickest and most accurate combination overall, while
animation with playback control the most preferred temporal encodin
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