43-Issue 3

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https://diglib.eg.org/handle/10.2312/3606991

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CUPID: Contextual Understanding of Prompt-conditioned Image Distributions
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Zhao, Yayan; Li, Mingwei; Berger, Matthew; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
We present CUPID: a visualization method for the contextual understanding of prompt-conditioned image distributions. CUPID targets the visual analysis of distributions produced by modern text-to-image generative models, wherein a user can specify a scene via natural language, and the model generates a set of images, each intended to satisfy the user's description. CUPID is designed to help understand the resulting distribution, using contextual cues to facilitate analysis: objects mentioned in the prompt, novel, synthesized objects not explicitly mentioned, and their potential relationships. Central to CUPID is a novel method for visualizing high-dimensional distributions, wherein contextualized embeddings of objects, those found within images, are mapped to a low-dimensional space via density-based embeddings. We show how such embeddings allows one to discover salient styles of objects within a distribution, as well as identify anomalous, or rare, object styles. Moreover, we introduce conditional density embeddings, whereby conditioning on a given object allows one to compare object dependencies within the distribution. We employ CUPID for analyzing image distributions produced by large-scale diffusion models, where our experimental results offer insights on language misunderstanding from such models and biases in object composition, while also providing an interface for discovery of typical, or rare, synthesized scenes.
Visual Highlighting for Situated Brushing and Linking
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Doerr, Nina; Lee, Benjamin; Baricova, Katarina; Schmalstieg, Dieter; Sedlmair, Michael; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
Brushing and linking is widely used for visual analytics in desktop environments. However, using this approach to link many data items between situated (e.g., a virtual screen with data) and embedded views (e.g., highlighted objects in the physical environment) is largely unexplored. To this end, we study the effectiveness of visual highlighting techniques in helping users identify and link physical referents to brushed data marks in a situated scatterplot. In an exploratory virtual reality user study (N=20), we evaluated four highlighting techniques under different physical layouts and tasks. We discuss the effectiveness of these techniques, as well as implications for the design of brushing and linking operations in situated analytics.
An Experimental Evaluation of Viewpoint-Based 3D Graph Drawing
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Wageningen, Simon van; Mchedlidze, Tamara; Telea, Alexandru; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
Node-link diagrams are a widely used metaphor for creating visualizations of relational data. Most frequently, such techniques address creating 2D graph drawings, which are easy to use on computer screens and in print. In contrast, 3D node-link graph visualizations are far less used, as they have many known limitations and comparatively few well-understood advantages. A key issue here is that such 3D visualizations require users to select suitable viewpoints. We address this limitation by studying the ability of layout techniques to produce high-quality views of 3D graph drawings. For this, we perform a thorough experimental evaluation, comparing 3D graph drawings, rendered from a covering sampling of all viewpoints, with their 2D counterparts across various state-of-the-art node-link drawing algorithms, graph families, and quality metrics. Our results show that, depending on the graph family, 3D node-link diagrams can contain a many viewpoints that yield 2D visualizations that are of higher quality than those created by directly using 2D node-link diagrams. This not only sheds light on the potential of 3D node-link diagrams but also gives a simple approach to produce high-quality 2D node-link diagrams.
InverseVis: Revealing the Hidden with Curved Sphere Tracing
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Lawonn, Kai; Meuschke, Monique; Günther, Tobias; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
Exploratory analysis of scalar fields on surface meshes presents significant challenges in identifying and visualizing important regions, particularly on the surface's backside. Previous visualization methods achieved only a limited visibility of significant features, i.e., regions with high or low scalar values, during interactive exploration. In response to this, we propose a novel technique, InverseVis, which leverages curved sphere tracing and uses the otherwise unused space to enhance visibility. Our approach combines direct and indirect rendering, allowing camera rays to wrap around the surface and reveal information from the backside. To achieve this, we formulate an energy term that guides the image synthesis in previously unused space, highlighting the most important regions of the backside. By quantifying the amount of visible important features, we optimize the camera position to maximize the visibility of the scalar field on both the front and backsides. InverseVis is benchmarked against state-of-the-art methods and a derived technique, showcasing its effectiveness in revealing essential features and outperforming existing approaches.
EuroVis 2024 CGF 43-3: Frontmatter
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
Exploring the Design Space of BioFabric Visualization for Multivariate Network Analysis
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Fuchs, Johannes; Dennig, Frederik L.; Heinle, Maria-Viktoria; Keim, Daniel A.; Bartolomeo, Sara Di; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
The visual analysis of multivariate network data is a common yet difficult task in many domains. The major challenge is to visualize the network's topology and additional attributes for entities and their connections. Although node-link diagrams and adjacency matrices are widespread, they have inherent limitations. Node-link diagrams struggle to scale effectively, while adjacency matrices can fail to represent network topologies clearly. In this paper, we delve into the design space of BioFabric, which aligns entities along rows and relationships along columns, providing a way to encapsulate multiple attributes for both. We explore how we can leverage the unique opportunities offered by BioFabric's design space to visualize multivariate network data - focusing on three main categories: juxtaposed visualizations, embedded on-node and on-edge encoding, and transformed node and edge encoding. We complement our exploration with a quantitative assessment comparing BioFabric to adjacency matrices. We postulate that the expansive design possibilities introduced in BioFabric network visualization have the potential for the visualization of multivariate data, and we advocate for further evaluation of the associated design space. Our supplemental material is available on osf.io.
Exploring Classifiers with Differentiable Decision Boundary Maps
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Machado, Alister; Behrisch, Michael; Telea, Alexandru; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
Explaining Machine Learning (ML) - and especially Deep Learning (DL) - classifiers' decisions is a subject of interest across fields due to the increasing ubiquity of such models in computing systems. As models get increasingly complex, relying on sophisticated machinery to recognize data patterns, explaining their behavior becomes more difficult. Directly visualizing classifier behavior is in general infeasible, as they create partitions of the data space, which is typically high dimensional. In recent years, Decision Boundary Maps (DBMs) have been developed, taking advantage of projection and inverse projection techniques. By being able to map 2D points back to the data space and subsequently run a classifier, DBMs represent a slice of classifier outputs. However, we recognize that DBMs without additional explanatory views are limited in their applicability. In this work, we propose augmenting the naive DBM generating process with views that provide more in-depth information about classifier behavior, such as whether the training procedure is locally stable. We describe our proposed views - which we term Differentiable Decision Boundary Maps - over a running example, explaining how our work enables drawing new and useful conclusions from these dense maps. We further demonstrate the value of these conclusions by showing how useful they would be in carrying out or preventing a dataset poisoning attack. We thus provide evidence of the ability of our proposed views to make DBMs significantly more trustworthy and interpretable, increasing their utility as a model understanding tool.
Topological Characterization and Uncertainty Visualization of Atmospheric Rivers
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Lan, Fangfei; Gamelin, Brandi; Yan, Lin; Wang, Jiali; Wang, Bei; Guo, Hanqi; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
Atmospheric rivers (ARs) are long, narrow regions of water vapor in the Earth's atmosphere that transport heat and moisture from the tropics to the mid-latitudes. ARs are often associated with extreme weather events in North America and contribute significantly to water supply and flood risk. However, characterizing ARs has been a major challenge due to the lack of a universal definition and their structural variations. Existing AR detection tools (ARDTs) produce distinct AR boundaries for the same event, making the risk assessment of ARs a difficult task. Understanding these uncertainties is crucial to improving the predictability of AR impacts, including their landfall areas and associated precipitation, which could cause catastrophic flooding and landslides over the coastal regions. In this work, we develop an uncertainty visualization framework that captures boundary and interior uncertainties, i.e., structural variations, of an ensemble of ARs that arise from a set of ARDTs. We first provide a statistical overview of the AR boundaries using the contour boxplots of Whitaker et al. that highlight the structural variations of AR boundaries based on their nesting relationships. We then introduce the topological skeletons of ARs based on Morse complexes that characterize the interior variation of an ensemble of ARs. We propose an uncertainty visualization of these topological skeletons, inspired by MetroSets of Jacobson et al. that emphasizes the agreements and disagreements across the ensemble members. Through case studies and expert feedback, we demonstrate that the two approaches complement each other, and together they could facilitate an effective comparative analysis process and provide a more confident outlook on an AR's shape, area, and onshore impact.
Improving Temporal Treemaps by Minimizing Crossings
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Dobler, Alexander; Nöllenburg, Martin; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
Temporal trees are trees that evolve over a discrete set of time steps. Each time step is associated with a node-weighted rooted tree and consecutive trees change by adding new nodes, removing nodes, splitting nodes, merging nodes, and changing node weights. Recently, two-dimensional visualizations of temporal trees called temporal treemaps have been proposed, representing the temporal dimension on the x-axis, and visualizing the tree modifications over time as temporal edges of varying thickness. The tree hierarchy at each time step is depicted as a vertical, one-dimensional nesting relationships, similarly to standard, nontemporal treemaps. Naturally, temporal edges can cross in the visualization, decreasing readability. Heuristics were proposed to minimize such crossings in the literature, but a formal characterization and minimization of crossings in temporal treemaps was left open. In this paper, we propose two variants of defining crossings in temporal treemaps that can be combinatorially characterized. For each variant, we propose an exact optimization algorithm based on integer linear programming and heuristics based on graph drawing techniques. In an extensive experimental evaluation, we show that on the one hand the exact algorithms reduce the number of crossings by a factor of 20 on average compared to the previous algorithms. On the other hand, our new heuristics are faster by a factor of more than 100 and still reduce the number of crossings by a factor of almost three.
psudo: Exploring Multi-Channel Biomedical Image Data with Spatially and Perceptually Optimized Pseudocoloring
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Warchol, Simon; Troidl, Jakob; Muhlich, Jeremy; Krueger, Robert; Hoffer, John; Lin, Tica; Beyer, Johanna; Glassman, Elena; Sorger, Peter; Pfister, Hanspeter; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
Over the past century, multichannel fluorescence imaging has been pivotal in myriad scientific breakthroughs by enabling the spatial visualization of proteins within a biological sample. With the shift to digital methods and visualization software, experts can now flexibly pseudocolor and combine image channels, each corresponding to a different protein, to explore their spatial relationships. We thus propose psudo, an interactive system that allows users to create optimal color palettes for multichannel spatial data. In psudo, a novel optimization method generates palettes that maximize the perceptual differences between channels while mitigating confusing color blending in overlapping channels. We integrate this method into a system that allows users to explore multi-channel image data and compare and evaluate color palettes for their data. An interactive lensing approach provides on-demand feedback on channel overlap and a color confusion metric while giving context to the underlying channel values. Color palettes can be applied globally or, using the lens, to local regions of interest. We evaluate our palette optimization approach using three graphical perception tasks in a crowdsourced user study with 150 participants, showing that users are more accurate at discerning and comparing the underlying data using our approach. Additionally, we showcase psudo in a case study exploring the complex immune responses in cancer tissue data with a biologist.
Persist: Persistent and Reusable Interactions in Computational Notebooks
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Gadhave, Kiran; Cutler, Zach; Lex, Alexander; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
Computational notebooks, such as Jupyter, support rich data visualization. However, even when visualizations in notebooks are interactive, they are a dead end: Interactive data manipulations, such as selections, applying labels, filters, categorizations, or fixes to column or cell values, could be efficiently applied in interactive visual components, but interactive components typically cannot manipulate Python data structures. Furthermore, actions performed in interactive plots are lost as soon as the cell is re-run, prohibiting reusability and reproducibility. To remedy this problem, we introduce Persist, a family of techniques to (a) capture interaction provenance, enabling the persistence of interactions, and (b) map interactions to data manipulations that can be applied to dataframes.We implement our approach as a JupyterLab extension that supports tracking interactions in Vega- Altair plots and in a data table view. Persist can re-execute interaction provenance when a notebook or a cell is re-executed, enabling reproducibility and re-use.We evaluate Persist in a user study targeting data manipulations with 11 participants skilled in Python and Pandas, comparing it to traditional code-based approaches. Participants were consistently faster and were able to correctly complete more tasks with Persist.
HORA 3D: Personalized Flood Risk Visualization as an Interactive Web Service
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Rauer-Zechmeister, Silvana; Cornel, Daniel; Sadransky, Bernhard; Horváth, Zsolt; Konev, Artem; Buttinger-Kreuzhuber, Andreas; Heidrich, Raimund; Blöschl, Günter; Gröller, Eduard; Waser, Jürgen; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
We propose an interactive web-based application to inform the general public about personal flood risks. Flooding is the natural hazard affecting most people worldwide. Protection against flooding is not limited to mitigation measures, but also includes communicating its risks to affected individuals to raise awareness and preparedness for its adverse effects. Until now, this is mostly done with static and indiscriminate 2D maps of the water depth. These flood hazard maps can be difficult to interpret and the user has to derive a personal flood risk based on prior knowledge. In addition to the hazard, the flood risk has to consider the exposure of the own house and premises to high water depths and flow velocities as well as the vulnerability of particular parts. Our application is centered around an interactive personalized visualization to raise awareness of these risk factors for an object of interest. We carefully extract and show only the relevant information from large precomputed flood simulation and geospatial data to keep the visualization simple and comprehensible. To achieve this goal, we extend various existing approaches and combine them with new real-time visualization and interaction techniques in 3D. A new view-dependent focus+context design guides user attention and supports an intuitive interpretation of the visualization to perform predefined exploration tasks. HORA 3D enables users to individually inform themselves about their flood risks. We evaluated the user experience through a broad online survey with 87 participants of different levels of expertise, who rated the helpfulness of the application with 4.7 out of 5 on average.
Antarstick: Extracting Snow Height From Time-Lapse Photography
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Lang, Matěj; Mráz, Radoslav; Trtík, Marek; Stoppel, Sergej; Byška, Jan; Kozlikova, Barbora; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
The evolution and accumulation of snow cover are among the most important characteristics influencing Antarctica's climate and biotopes. The changes in Antarctica are also substantially impacting global climate change. Therefore, detailed monitoring of snow evolution is key to understanding such changes. One way to conduct this monitoring is by installing trail cameras in a particular region and then processing the captured information. This option is affordable, but has some drawbacks, such as the fully automatic solution for the extraction of snow height from these images is not feasible. Therefore, it still requires human intervention, manually correcting the inaccurately extracted information. In this paper, we present Antarstick, a tool for visual guidance of the user to potentially wrong values extracted from poor-quality images and support for their interactive correction. This tool allows for much quicker and semi-automated processing of snow height from time-lapse photography.
CAN: Concept-aligned Neurons for Visual Comparison of Neural Networks
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Li, Mingwei; Jeong, Sangwon; Liu, Shusen; Berger, Matthew; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
We present concept-aligned neurons, or CAN, a visualization design for comparing deep neural networks. The goal of CAN is to support users in understanding the similarities and differences between neural networks, with an emphasis on comparing neuron functionality across different models. To make this comparison intuitive, CAN uses concept-based representations of neurons to visually align models in an interpretable manner. A key feature of CAN is the hierarchical organization of concepts, which permits users to relate sets of neurons at different levels of detail. CAN's visualization is designed to help compare the semantic coverage of neurons, as well as assess the distinctiveness, redundancy, and multi-semantic alignment of neurons or groups of neurons, all at different concept granularity. We demonstrate the generality and effectiveness of CAN by comparing models trained on different datasets, neural networks with different architectures, and models trained for different objectives, e.g. adversarial robustness, and robustness to out-of-distribution data.
Sparse q-ball imaging towards efficient visual exploration of HARDI data
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Lei, Danhua; Miandji, Ehsan; Unger, Jonas; Hotz, Ingrid; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
Diffusion-weighted magnetic resonance imaging (D-MRI) is a technique to measure the diffusion of water, in biological tissues. It is used to detect microscopic patterns, such as neural fibers in the living human brain, with many medical and neuroscience applications e.g. for fiber tracking. In this paper, we consider High-Angular Resolution Diffusion Imaging (HARDI) which provides one of the richest representations of water diffusion. It records the movement of water molecules by measuring diffusion under 64 or more directions. A key challenge is that it generates high-dimensional, large, and complex datasets. In our work, we develop a novel representation that exploits the inherent sparsity of the HARDI signal by approximating it as a linear sum of basic atoms in an overcomplete data-driven dictionary using only a sparse set of coefficients. We show that this approach can be efficiently integrated into the standard q-ball imaging pipeline to compute the diffusion orientation distribution function (ODF). Sparse representations have the potential to reduce the size of the data while also giving some insight into the data. To explore the results, we provide a visualization of the atoms of the dictionary and their frequency in the data to highlight the basic characteristics of the data. We present our proposed pipeline and demonstrate its performance on 5 HARDI datasets.
ChoreoVis: Planning and Assessing Formations in Dance Choreographies
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Beck, Samuel; Doerr, Nina; Kurzhals, Kuno; Riedlinger, Alexander; Schmierer, Fabian; Sedlmair, Michael; Koch, Steffen; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
Sports visualization has developed into an active research field over the last decades. Many approaches focus on analyzing movement data recorded from unstructured situations, such as soccer. For the analysis of choreographed activities like formation dancing, however, the goal differs, as dancers follow specific formations in coordinated movement trajectories. To date, little work exists on how visual analytics methods can support such choreographed performances. To fill this gap, we introduce a new visual approach for planning and assessing dance choreographies. In terms of planning choreographies, we contribute a web application with interactive authoring tools and views for the dancers' positions and orientations, movement trajectories, poses, dance floor utilization, and movement distances. For assessing dancers' real-world movement trajectories, extracted by manual bounding box annotations, we developed a timeline showing aggregated trajectory deviations and a dance floor view for detailed trajectory comparison. Our approach was developed and evaluated in collaboration with dance instructors, showing that introducing visual analytics into this domain promises improvements in training efficiency for the future.
Interactive Optimization for Cartographic Aggregation of Building Features
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Takahashi, Shigeo; Kokubun, Ryo; Nishimura, Satoshi; Misue, Kazuo; Arikawa, Masatoshi; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
Aggregation, as an operation of cartographic generalization, provides an effective means of abstracting the configuration of building features by combining them according to the scale reduction of the 2D map. Automating this design process effectively helps professional cartographers design both paper and digital maps, but finding the best aggregation result from the numerous combinations of building features has been a challenge. This paper presents a novel approach to assist cartographers in interactively designing the aggregation of building features in scale-aware map visualization. Our contribution is to provide an appropriate set of candidates for the cartographer to choose from among a limited number of possible combinations of building features. This is achieved by collecting locally optimal solutions that emerge in the course of aggregation operations, formulated as a label cost optimization problem. Users can also explore better aggregation results by interactively adjusting the design parameters to update the set of possible combinations, along with an operator to force the combination of manually selected building features. Each cluster of aggregated building features is tightly enclosed by a concave hull, which is later adaptively simplified to abstract its boundary shapes. Experimental design examples and evaluations by expert cartographers demonstrate the feasibility of the proposed approach to interactive aggregation.
Instantaneous Visual Analysis of Blood Flow in Stenoses Using Morphological Similarity
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Eulzer, Pepe; Richter, Kevin; Hundertmark, Anna; Wickenhoefer, Ralph; Klingner, Carsten; Lawonn, Kai; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
The emergence of computational fluid dynamics (CFD) enabled the simulation of intricate transport processes, including flow in physiological structures, such as blood vessels. While these so-called hemodynamic simulations offer groundbreaking opportunities to solve problems at the clinical forefront, a successful translation of CFD to clinical decision-making is challenging. Hemodynamic simulations are intrinsically complex, time-consuming, and resource-intensive, which conflicts with the timesensitive nature of clinical workflows and the fact that hospitals usually do not have the necessary resources or infrastructure to support CFD simulations. To address these transfer challenges, we propose a novel visualization system which enables instant flow exploration without performing on-site simulation. To gain insights into the viability of the approach, we focus on hemodynamic simulations of the carotid bifurcation, which is a highly relevant arterial subtree in stroke diagnostics and prevention. We created an initial database of 120 high-resolution carotid bifurcation flow models and developed a set of similarity metrics used to place a new carotid surface model into a neighborhood of simulated cases with the highest geometric similarity. The neighborhood can be immediately explored and the flow fields analyzed.We found that if the artery models are similar enough in the regions of interest, a new simulation leads to coinciding results, allowing the user to circumvent individual flow simulations. We conclude that similarity-based visual analysis is a promising approach toward the usability of CFD in medical practice.
From Delays to Densities: Exploring Data Uncertainty through Speech, Text, and Visualization
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Stokes, Chase; Sanker, Chelsea; Cogley, Bridget; Setlur, Vidya; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
Understanding and communicating data uncertainty is crucial for making informed decisions in sectors like finance and healthcare. Previous work has explored how to express uncertainty in various modes. For example, uncertainty can be expressed visually with quantile dot plots or linguistically with hedge words and prosody. Our research aims to systematically explore how variations within each mode contribute to communicating uncertainty to the user; this allows us to better understand each mode's affordances and limitations. We completed an exploration of the uncertainty design space based on pilot studies and ran two crowdsourced experiments examining how speech, text, and visualization modes and variants within them impact decision-making with uncertain data. Visualization and text were most effective for rational decision-making, though text resulted in lower confidence. Speech garnered the highest trust despite sometimes leading to risky decisions. Results from these studies indicate meaningful trade-offs among modes of information and encourage exploration of multimodal data representations.
DynTrix: A Hybrid Representation for Dynamic Graphs
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Vago, Benjamin; Archambault, Daniel; Arleo, Alessio; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
Hybrid graph representations combine two or more network visualization techniques in a unique drawing, simultaneously leveraging their strong traits. Since their introduction in the early 2000s, hybrid representations have gained significant research interest, with the introduction of new techniques and comparative user studies. However, all this research has not considered dynamic graphs. In this paper, we investigate hybrid graph representations in a dynamic network context and present DynTrix. Our system uses the NodeTrix representation as a basis, but the research extends this representation to the dynamic network domain. DynTrix supports automatic or manually created clusters/matrices across time. Drawing stability is implemented through aggregation and users can rearrange the nodes/matrix positions and pin them. DynTrix visualizes the temporal dynamics of the network through a combination of movement and element highlighting. We also introduce the concept of volatility, that allows the identification of actors in the network that are the most volatile. Matrices can be ordered such that stable cores gravitate towards the centre of the matrix. We integrate this technique in a visual analytics application for the exploration of offline dynamic networks and evaluate our system through case studies and qualitative expert interviews. Experts agree on the capabilities of the system, noting its potential for the analysis of dynamic networks through hybrid representations.

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