41-Issue 3

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https://diglib.eg.org/handle/10.2312/2633194

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ModelWise: Interactive Model Comparison for Model Diagnosis, Improvement and Selection
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Meng, Linhao; Elzen, Stef van den; Vilanova, Anna; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
Model comparison is an important process to facilitate model diagnosis, improvement, and selection when multiple models are developed for a classification task. It involves careful comparison concerning model performance and interpretation. Current visual analytics solutions often ignore the feature selection process. They either do not support detailed analysis of multiple multi-class classifiers or rely on feature analysis alone to interpret model results. Understanding how different models make classification decisions, especially classification disagreements of the same instances, requires a deeper model understanding. We present ModelWise, a visual analytics method to compare multiple multi-class classifiers in terms of model performance, feature space, and model explanation. ModelWise adapts visualizations with rich interactions to support multiple workflows to achieve model diagnosis, improvement, and selection. It considers feature subspaces generated for use in different models and improves model understanding by model explanation. We demonstrate the usability of ModelWise with two case studies, one with a small exemplar dataset and another developed with a machine learning expert with real-world perioperative data.
Of Course it's Political! A Critical Inquiry into Underemphasized Dimensions in Civic Text Visualization
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Baumer, Eric P. S.; Jasim, Mahmood; Sarvghad, Ali; Mahyar, Narges; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
Recent developments in critical information visualization have brought the field's attention to political, feminist, ethical, and rhetorical aspects of data visualization. However, less work has explored the interplay between design decisions and political ramifications-structures of authority, means of representation, etc. In this paper, we build upon these critical perspectives and highlight the political aspect of civic text visualization especially in the context of democratic decision-making. Based on a critical analysis of survey papers about text visualization in general, followed by a review on the status quo of text visualization in civics, we argue that civic text visualization inherits an exclusively analytic framing. This framing leads to a series of issues and challenges in the fundamentally political context of civics, such as misinterpretation of data, missing minority voices, and excluding the public from decision making processes. To span this gap between political context and analytic framing, we provide a series of two-pole conceptual dimensions, such as from singular user to multiple relationships, and from complexity to inclusivity of visualization design. For each dimension, we discuss how the tensions between these poles can help surface the political ramifications of design decisions in civic text visualization. These dimensions can thus help visualization researchers, designers, and practitioners attend more intentionally to these political aspects and inspire their design choices. We conclude by suggesting that these dimensions may be useful for visualization design across a variety of application domains, beyond civic text visualization.
Rich Screen Reader Experiences for Accessible Data Visualization
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Zong, Jonathan; Lee, Crystal; Lundgard, Alan; Jang, JiWoong; Hajas, Daniel; Satyanarayan, Arvind; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
Current web accessibility guidelines ask visualization designers to support screen readers via basic non-visual alternatives like textual descriptions and access to raw data tables. But charts do more than summarize data or reproduce tables; they afford interactive data exploration at varying levels of granularity-from fine-grained datum-by-datum reading to skimming and surfacing high-level trends. In response to the lack of comparable non-visual affordances, we present a set of rich screen reader experiences for accessible data visualization and exploration. Through an iterative co-design process, we identify three key design dimensions for expressive screen reader accessibility: structure, or how chart entities should be organized for a screen reader to traverse; navigation, or the structural, spatial, and targeted operations a user might perform to step through the structure; and, description, or the semantic content, composition, and verbosity of the screen reader's narration. We operationalize these dimensions to prototype screen-reader-accessible visualizations that cover a diverse range of chart types and combinations of our design dimensions. We evaluate a subset of these prototypes in a mixed-methods study with 13 blind and visually impaired readers. Our findings demonstrate that these designs help users conceptualize data spatially, selectively attend to data of interest at different levels of granularity, and experience control and agency over their data analysis process.
A Typology of Guidance Tasks in Mixed-Initiative Visual Analytics Environments
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Pérez-Messina, Ignacio; Ceneda, Davide; El-Assady, Mennatallah; Miksch, Silvia; Sperrle, Fabian; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
Guidance has been proposed as a conceptual framework to understand how mixed-initiative visual analytics approaches can actively support users as they solve analytical tasks. While user tasks received a fair share of attention, it is still not completely clear how they could be supported with guidance and how such support could influence the progress of the task itself. Our observation is that there is a research gap in understanding the effect of guidance on the analytical discourse, in particular, for the knowledge generation in mixed-initiative approaches. As a consequence, guidance in a visual analytics environment is usually indistinguishable from common visualization features, making user responses challenging to predict and measure. To address these issues, we take a system perspective to propose the notion of guidance tasks and we present it as a typology closely aligned to established user task typologies. We derived the proposed typology directly from a model of guidance in the knowledge generation process and illustrate its implications for guidance design. By discussing three case studies, we show how our typology can be applied to analyze existing guidance systems. We argue that without a clear consideration of the system perspective, the analysis of tasks in mixed-initiative approaches is incomplete. Finally, by analyzing matchings of user and guidance tasks, we describe how guidance tasks could either help the user conclude the analysis or change its course.
DanmuVis: Visualizing Danmu Content Dynamics and Associated Viewer Behaviors in Online Videos
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Chen, Shuai; Li, Sihang; Li, Yanda; Zhu, Junlin; Long, Juanjuan; Chen, Siming; Zhang, Jiawan; Yuan, Xiaoru; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
Danmu (Danmaku) is a unique social media service in online videos, especially popular in Japan and China, for viewers to write comments while watching videos. The danmu comments are overlaid on the video screen and synchronized to the associated video time, indicating viewers' thoughts of the video clip. This paper introduces an interactive visualization system to analyze danmu comments and associated viewer behaviors in a collection of videos and enable detailed exploration of one video on demand. The watching behaviors of viewers are identified by comparing video time and post time of viewers' danmu. The system supports analyzing danmu content and viewers' behaviors against both video time and post time to gain insights into viewers' online participation and perceived experience. Our evaluations, including usage scenarios and user interviews, demonstrate the effectiveness and usability of our system.
CorpusVis: Visual Analysis of Digital Sheet Music Collections
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Miller, Matthias; Rauscher, Julius; Keim, Daniel A.; El-Assady, Mennatallah; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
Manually investigating sheet music collections is challenging for music analysts due to the magnitude and complexity of underlying features, structures, and contextual information. However, applying sophisticated algorithmic methods would require advanced technical expertise that analysts do not necessarily have. Bridging this gap, we contribute CorpusVis, an interactive visual workspace, enabling scalable and multi-faceted analysis. Our proposed visual analytics dashboard provides access to computational methods, generating varying perspectives on the same data. The proposed application uses metadata including composers, type, epoch, and low-level features, such as pitch, melody, and rhythm. To evaluate our approach, we conducted a pair-analytics study with nine participants. The qualitative results show that CorpusVis supports users in performing exploratory and confirmatory analysis, leading them to new insights and findings. In addition, based on three exemplary workflows, we demonstrate how to apply our approach to different tasks, such as exploring musical features or comparing composers.
Interactively Assessing Disentanglement in GANs
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Jeong, Sangwon; Liu, Shusen; Berger, Matthew; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
Generative adversarial networks (GAN) have witnessed tremendous growth in recent years, demonstrating wide applicability in many domains. However, GANs remain notoriously difficult for people to interpret, particularly for modern GANs capable of generating photo-realistic imagery. In this work we contribute a visual analytics approach for GAN interpretability, where we focus on the analysis and visualization of GAN disentanglement. Disentanglement is concerned with the ability to control content produced by a GAN along a small number of distinct, yet semantic, factors of variation. The goal of our approach is to shed insight on GAN disentanglement, above and beyond coarse summaries, instead permitting a deeper analysis of the data distribution modeled by a GAN. Our visualization allows one to assess a single factor of variation in terms of groupings and trends in the data distribution, where our analysis seeks to relate the learned representation space of GANs with attribute-based semantic scoring of images produced by GANs. Through use-cases, we show that our visualization is effective in assessing disentanglement, allowing one to quickly recognize a factor of variation and its overall quality. In addition, we show how our approach can highlight potential dataset biases learned by GANs.
Branch Decomposition-Independent Edit Distances for Merge Trees
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Wetzels, Florian; Leitte, Heike; Garth, Christoph; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
Edit distances between merge trees of scalar fields have many applications in scientific visualization, such as ensemble analysis, feature tracking or symmetry detection. In this paper, we propose branch mappings, a novel approach to the construction of edit mappings for merge trees. Classic edit mappings match nodes or edges of two trees onto each other, and therefore have to either rely on branch decompositions of both trees or have to use auxiliary node properties to determine a matching. In contrast, branch mappings employ branch properties instead of node similarity information, and are independent of predetermined branch decompositions. Especially for topological features, which are typically based on branch properties, this allows a more intuitive distance measure which is also less susceptible to instabilities from small-scale perturbations. For trees with O(n) nodes, we describe an O(n4) algorithm for computing optimal branch mappings, which is faster than the only other branch decomposition-independent method in the literature by more than a linear factor. Furthermore, we compare the results of our method on synthetic and real-world examples to demonstrate its practicality and utility.
LOOPS: LOcally Optimized Polygon Simplification
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Amiraghdam, Alireza; Diehl, Alexandra; Pajarola, Renato; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
Displaying polygonal vector data is essential in various application scenarios such as geometry visualization, vector graphics rendering, CAD drawing and in particular geographic, or cartographic visualization. Dealing with static polygonal datasets that has a large scale and are highly detailed poses several challenges to the efficient and adaptive display of polygons in interactive geographic visualization applications. For linear vector data, only recently a GPU-based level-of-detail (LOD) polyline simplification and rendering approach has been presented which can perform locally-adaptive LOD visualization of large-scale line datasets interactively. However, locally optimized LOD simplification and interactive display of large-scale polygon data, consisting of filled vector line loops, remains still a challenge, specifically in 3D geographic visualizations where varying LOD over a scene is necessary. Our solution to this challenge is a novel technique for locally-optimized simplification and visualization of 2D polygons over a 3D terrain which features a parallelized point-inside-polygon testing mechanism. Our approach is capable of employing any simplification algorithm that sequentially removes vertices such as Douglas-Peucker and Wang-Müller. Moreover, we generalized our technique to also visualizing polylines in order to have a unified method for displaying both data types. The results and performance analysis show that our new algorithm can handle large datasets containing polygons composed of millions of segments in real time, and has a lower memory demand and higher performance in comparison to prior methods of line simplification and visualization.
Exploring Effects of Ecological Visual Analytics Interfaces on Experts' and Novices' Decision-Making Processes: A Case Study in Air Traffic Control
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Zohrevandi, Elmira; Westin, Carl A. L.; Vrotsou, Katerina; Lundberg, Jonas; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
Operational demands in safety-critical systems impose a risk of failure to the operators especially during urgent situations. Operators of safety-critical systems learn to make decisions effectively throughout extensive training programs and many years of experience. In the domain of air traffic control, expensive training with high dropout rates calls for research to enhance novices' ability to detect and resolve conflicts in the airspace. While previous researchers have mostly focused on redesigning training instructions and programs, the current paper explores possible benefits of novel visual representations to improve novices' understanding of the situations as well as their decision-making process. We conduct an experimental evaluation study testing two ecological visual analytics interfaces, developed in a previous study, as support systems to facilitate novice decisionmaking. The main contribution of this paper is threefold. First, we describe the application of an ecological interface design approach to the development of two visual analytics interfaces. Second, we perform a human-in-the-loop experiment with fortyfive novices within a simplified air traffic control simulation environment. Third, by performing an expert-novice comparison we investigate the extent to which effects of the proposed interfaces can be attributed to the subjects' expertise. The results show that the proposed ecological visual analytics interfaces improved novices' understanding of the information about conflicts as well as their problem-solving performance. Further, the results show that the beneficial effects of the proposed interfaces were more attributable to the visual representations than the users' expertise.
How Accessible is my Visualization? Evaluating Visualization Accessibility with Chartability
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Elavsky, Frank; Bennett, Cynthia; Moritz, Dominik; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
Novices and experts have struggled to evaluate the accessibility of data visualizations because there are no common shared guidelines across environments, platforms, and contexts in which data visualizations are authored. Between non-specific standards bodies like WCAG, emerging research, and guidelines from specific communities of practice, it is hard to organize knowledge on how to evaluate accessible data visualizations. We present Chartability, a set of heuristics synthesized from these various sources which enables designers, developers, researchers, and auditors to evaluate data-driven visualizations and interfaces for visual, motor, vestibular, neurological, and cognitive accessibility. In this paper, we outline our process of making a set of heuristics and accessibility principles for Chartability and highlight key features in the auditing process. Working with participants on real projects, we found that data practitioners with a novice level of accessibility skills were more confident and found auditing to be easier after using Chartability. Expert accessibility practitioners were eager to integrate Chartability into their own work. Reflecting on Chartability's development and the preliminary user evaluation, we discuss tradeoffs of open projects, working with high-risk evaluations like auditing projects in the wild, and challenge future research projects at the intersection of visualization and accessibility to consider the broad intersections of disabilities.
SimilarityNet: A Deep Neural Network for Similarity Analysis Within Spatio-temporal Ensembles
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Huesmann, Karim; Linsen, Lars; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
Latent feature spaces of deep neural networks are frequently used to effectively capture semantic characteristics of a given dataset. In the context of spatio-temporal ensemble data, the latent space represents a similarity space without the need of an explicit definition of a field similarity measure. Commonly, these networks are trained for specific data within a targeted application. We instead propose a general training strategy in conjunction with a deep neural network architecture, which is readily applicable to any spatio-temporal ensemble data without re-training. The latent-space visualization allows for a comprehensive visual analysis of patterns and temporal evolution within the ensemble. With the use of SimilarityNet, we are able to perform similarity analyses on large-scale spatio-temporal ensembles in less than a second on commodity consumer hardware. We qualitatively compare our results to visualizations with established field similarity measures to document the interpretability of our latent space visualizations and show that they are feasible for an in-depth basic understanding of the underlying temporal evolution of a given ensemble.
Hybrid Touch/Tangible Spatial Selection in Augmented Reality
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Sereno, Mickael; Gosset, Stéphane; Besançon, Lonni; Isenberg, Tobias; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
We study tangible touch tablets combined with Augmented Reality Head-Mounted Displays (AR-HMDs) to perform spatial 3D selections. We are primarily interested in the exploration of 3D unstructured datasets such as cloud points or volumetric datasets. AR-HMDs immerse users by showing datasets stereoscopically, and tablets provide a set of 2D exploration tools. Because AR-HMDs merge the visualization, interaction, and the users' physical spaces, users can also use the tablets as tangible objects in their 3D space. Nonetheless, the tablets' touch displays provide their own visualization and interaction spaces, separated from those of the AR-HMD. This raises several research questions compared to traditional setups. In this paper, we theorize, discuss, and study different available mappings for manual spatial selections using a tangible tablet within an AR-HMD space. We then study the use of this tablet within a 3D AR environment, compared to its use with a 2D external screen.
Leveraging Analysis History for Improved In Situ Visualization Recommendation
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Epperson, Will; Lee, Doris Jung-Lin; Wang, Leijie; Agarwal, Kunal; Parameswaran, Aditya G.; Moritz, Dominik; Perer, Adam; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
Existing visualization recommendation systems commonly rely on a single snapshot of a dataset to suggest visualizations to users. However, exploratory data analysis involves a series of related interactions with a dataset over time rather than one-off analytical steps. We present Solas, a tool that tracks the history of a user's data analysis, models their interest in each column, and uses this information to provide visualization recommendations, all within the user's native analytical environment. Recommending with analysis history improves visualizations in three primary ways: task-specific visualizations use the provenance of data to provide sensible encodings for common analysis functions, aggregated history is used to rank visualizations by our model of a user's interest in each column, and column data types are inferred based on applied operations. We present a usage scenario and a user evaluation demonstrating how leveraging analysis history improves in situ visualization recommendations on real-world analysis tasks.
LMFingerprints: Visual Explanations of Language Model Embedding Spaces through Layerwise Contextualization Scores
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Sevastjanova, Rita; Kalouli, Aikaterini-Lida; Beck, Christin; Hauptmann, Hanna; El-Assady, Mennatallah; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
Language models, such as BERT, construct multiple, contextualized embeddings for each word occurrence in a corpus. Understanding how the contextualization propagates through the model's layers is crucial for deciding which layers to use for a specific analysis task. Currently, most embedding spaces are explained by probing classifiers; however, some findings remain inconclusive. In this paper, we present LMFingerprints, a novel scoring-based technique for the explanation of contextualized word embeddings. We introduce two categories of scoring functions, which measure (1) the degree of contextualization, i.e., the layerwise changes in the embedding vectors, and (2) the type of contextualization, i.e., the captured context information. We integrate these scores into an interactive explanation workspace. By combining visual and verbal elements, we provide an overview of contextualization in six popular transformer-based language models. We evaluate hypotheses from the domain of computational linguistics, and our results not only confirm findings from related work but also reveal new aspects about the information captured in the embedding spaces. For instance, we show that while numbers are poorly contextualized, stopwords have an unexpected high contextualization in the models' upper layers, where their neighborhoods shift from similar functionality tokens to tokens that contribute to the meaning of the surrounding sentences.
Infographics Wizard: Flexible Infographics Authoring and Design Exploration
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Tyagi, Anjul; Zhao, Jian; Patel, Pushkar; Khurana, Swasti; Mueller, Klaus; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
Infographics are an aesthetic visual representation of information following specific design principles of human perception. Designing infographics can be a tedious process for non-experts and time-consuming, even for professional designers. With the help of designers, we propose a semi-automated infographic framework for general structured and flow-based infographic design generation. For novice designers, our framework automatically creates and ranks infographic designs for a user-provided text with no requirement for design input. However, expert designers can still provide custom design inputs to customize the infographics. We will also contribute an individual visual group (VG) designs dataset (in SVG), along with a 1k complete infographic image dataset with segmented VGs in this work. Evaluation results confirm that by using our framework, designers from all expertise levels can generate generic infographic designs faster than existing methods while maintaining the same quality as hand-designed infographics templates.
An Interactive Approach for Identifying Structure Definitions
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Mikula, Natalia; Dörffel, Tom; Baum, Daniel; Hege, Hans-Christian; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
Our ability to grasp and understand complex phenomena is essentially based on recognizing structures and relating these to each other. For example, any meteorological description of a weather condition and explanation of its evolution recurs to meteorological structures, such as convection and circulation structures, cloud fields and rain fronts. All of these are spatiotemporal structures, defined by time-dependent patterns in the underlying fields. Typically, such a structure is defined by a verbal description that corresponds to the more or less uniform, often somewhat vague mental images of the experts. However, a precise, formal definition of the structures or, more generally, of the concepts is often desirable, e.g., to enable automated data analysis or the development of phenomenological models. Here, we present a systematic approach and an interactive tool to obtain formal definitions of spatiotemporal structures. The tool enables experts to evaluate and compare different structure definitions on the basis of data sets with time-dependent fields that contain the respective structure. Since structure definitions are typically parameterized, an essential part is to identify parameter ranges that lead to desired structures in all time steps. In addition, it is important to allow a quantitative assessment of the resulting structures simultaneously. We demonstrate the use of the tool by applying it to two meteorological examples: finding structure definitions for vortex cores and center lines of temporarily evolving tropical cyclones. Ideally, structure definitions should be objective and applicable to as many data sets as possible. However, finding such definitions, e.g., for the common atmospheric structures in meteorology, can only be a long-term goal. The proposed procedure, together with the presented tool, is just a first systematic approach aiming at facilitating this long and arduous way.
Where did my Lines go? Visualizing Missing Data in Parallel Coordinates
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Bäuerle, Alex; Onzenoodt, Christian van; Kinderen, Simon der; Westberg, Jimmy Johansson; Jönsson, Daniel; Ropinski, Timo; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
We evaluate visualization concepts to represent missing values in parallel coordinates. We focus on the trade-off between the ability to perceive missing values and the concept's impact on common tasks. For this purpose, we identified three missing value representation concepts: removing line segments where values are missing, adding a separate, horizontal axis onto which missing values are projected, and using imputed values as a replacement for missing values. For the missing values axis and imputed values concepts, we additionally add downplay and highlight variations. We performed a crowd-sourced, quantitative user study with 732 participants comparing the concepts and their variations using five real-world datasets. Based on our findings, we provide suggestions regarding which visual encoding to employ depending on the task at focus.
Optimizing Grid Layouts for Level-of-Detail Exploration of Large Data Collections
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Frey, Steffen; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
This paper introduces an optimization approach for generating grid layouts from large data collections such that they are amenable to level-of-detail presentation and exploration. Classic (flat) grid layouts visually do not scale to large collections, yielding overwhelming numbers of tiny member representations. The proposed local search-based progressive optimization scheme generates hierarchical grids: leaves correspond to one grid cell and represent one member, while inner nodes cover a quadratic range of cells and convey an aggregate of contained members. The scheme is solely based on pairwise distances and jointly optimizes for homogeneity within inner nodes and across grid neighbors. The generated grids allow to present and flexibly explore the whole data collection with arbitrary local granularity. Diverse use cases featuring large data collections exemplify the application: stock market predictions from a Black-Scholes model, channel structures in soil from Markov chain Monte Carlo, and image collections with feature vectors from neural network classification models. The paper presents feedback by a domain scientist, compares against previous approaches, and demonstrates visual and computational scalability to a million members, surpassing classic grid layout techniques by orders of magnitude.
Six Methods for Transforming Layered Hypergraphs to Apply Layered Graph Layout Algorithms
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Bartolomeo, Sara Di; Pister, Alexis; Buono, Paolo; Plaisant, Catherine; Dunne, Cody; Fekete, Jean-Daniel; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
Hypergraphs are a generalization of graphs in which edges (hyperedges) can connect more than two vertices-as opposed to ordinary graphs where edges involve only two vertices. Hypergraphs are a fairly common data structure but there is little consensus on how to visualize them. To optimize a hypergraph drawing for readability, we need a layout algorithm. Common graph layout algorithms only consider ordinary graphs and do not take hyperedges into account. We focus on layered hypergraphs, a particular class of hypergraphs that, like layered graphs, assigns every vertex to a layer, and the vertices in a layer are drawn aligned on a linear axis with the axes arranged in parallel. In this paper, we propose a general method to apply layered graph layout algorithms to layered hypergraphs. We introduce six different transformations for layered hypergraphs. The choice of transformation affects the subsequent graph layout algorithm in terms of computational performance and readability of the results. Thus, we perform a comparative evaluation of these transformations in terms of number of crossings, edge length, and impact on performance. We also provide two case studies showing how our transformations can be applied to real-life use cases.

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