Human perception of graph drawings is influenced by a variety of impact factors for which quality measures are used as a proxy indicator. The investigation of these impact factors and their effects is important for evaluating and improving quality measures and drawing algorithms, as well as improving our understanding of human graph reading. The number of edge crossings in a 2D graph drawing has long been a main quality measure for drawing evaluation. The use of stereoscopic 3D graph visualisations has gained traction over the last years, and results from several studies indicate that they can improve analysis efficiency for a range of analysis scenarios. While edge crossings can also occur in 3D, there are additional edge configurations in space that are not crossings but might be perceived as such from a specific viewpoint. Such configurations create crossings when projected on the corresponding 2D image plane and could impact readability similar to 2D crossings. In 3D drawings, the additional depth aspect and the subsequent impact factors of edge distance and relative edge direction in space might further influence the importance of those configurations for readability. As a main contribution, we for the first time discuss potential impact factors. We discuss hypotheses on their impact, and as an initial investigation explore the impact of three selected factors in an empirical study.

Problem solving is a composite cognitive process, invoking a number of cognitive mechanisms, such as perception and memory. Individuals may form collectives to solve a given problem together in collaboration, especially when complexity is perceived to be high. To determine if and when collaborative problem solving is desired in the context of visual graph analysis, we compare ad hoc pairs to individuals and nominal pairs, when solving different tasks in mixed reality. We discuss the results of an experiment with 72 participants performed in two countries and three languages. We apply the concept of task instance complexity to quantify the visual demand of tasks used in the experiment. Our results show the importance of using nominal groups as a benchmark for evaluating collaborative virtual environments. We conclude that 3D graph representation is not sufficient to induce better collaborative results compared to the benchmark.

As Immersive Analytics (IA) increasingly uses Virtual Reality (VR) for stereoscopic 3D (S3D) graph visualisation, it is crucial to understand how users perceive network structures in these immersive environments. However, little is known about how humans read S3D graphs during task solving, and how gaze behaviour indicates task performance. To address this gap, we report a user study with 18 participants asked to perform three analytical tasks on S3D graph visualisations in a VR environment. Our findings reveal systematic relationships between network structural properties and gaze behaviour. Based on these insights, we contribute a comprehensive eye tracking methodology for analysing human perception in immersive environments and establish eye tracking as a valuable tool for objectively evaluating cognitive load in S3D graph visualisation.

Edge bundling is a technique commonly used to reduce visual clutter and improve the comprehension of the drawings of large graphs. Here, we model edge bundling as a multi-objective optimization problem and employ clustering strategies, metaheuristic and Pareto analysis to identify non-dominated solutions for some classical graphs from the literature.

Human perception and understanding of graph drawings is influenced by a variety of impact factors for which quality measures such as the number of crossings are used as a proxy indicator. For the more and more common stereoscopic 3D (S3D) graph visualisations, evidence is required to better understand graph perception and its relation to quality measures. We investigate the perception of crossing configurations in S3D graph visualisations and present the results of a study.

In recent years, licensed nursery schools in Japan have been evaluated from various perspectives. The Ministry of Health, Labour and Welfare (MHLW) encourages local governments and operating organizations to undergo third-party evaluations to improve staff morale and gain parental trust. However, the results of these evaluations are usually presented as plain text for each item. This makes it difficult to compare and interpret the data intuitively. To address this issue, we developed a visualization system to support efficient analysis and understanding of third-party evaluation data. The system is designed for educational specialists from organizations and local governments, rather than childcare professionals. We collected evaluation data for licensed nursery schools in Bunkyo-ku, Tokyo, and applied clustering to group similar schools. This system maps the results using color coding to enhance visual clarity. We also implemented co-occurrence network analysis to visualize key expressions and reveal similarities and differences among nursery schools. This system improves the readability of evaluation content and enables intuitive comparative analysis across multiple nursery schools.

Analysts often have to work with and make sense of large complex networks. One possible solution is to make visualisations interactive, providing users with a way to control visual clutter. Although several interactive methods have been proposed, there may be situations where some of them are too specific to be directly applicable. We have therefore identified several underlying low-level visual transformations, steered by group structures in the networks, and investigated their individual effects on user performance. This may both facilitate the development of further methods and support the generation of new hypotheses. We conducted an exploratory online experiment with 300 participants, involving five tasks, one control condition, and five group-based visual transformations: de-emphasising groups by opacity, position or size, aggregating groups, and hiding groups. The results for the three tasks that were specifically referring to groups show a high usage of the visual transformations by participants and several positive effects of the latter on accuracy, completion time, and mental effort spent. On the other hand, the two tasks that were not directly referring to groups show a lower usage of the visual transformations and the results regarding effects are rather mixed.

Celtic knots, an ancient art form often linked to Celtic heritage, have been used historically in the decoration of monuments and manuscripts, often symbolizing the notions of eternity and interconnectedness. This paper introduces the framework CelticGraph designed for illustrating graphs in the style of Celtic knots and links. The process of creating these drawings raises interesting combinatorial concepts in the theory of circuits in planar graphs. Further, CelticGraph uses a novel algorithm to represent edges as Bézier curves, aiming to show each link as a smooth curve with limited curvature. We also show that with our production mechanisms we can compute any 4-regular plane graph and thereby any celtic knot or link. The CelticGraph framework for drawing graphs as celtic knots and links is implemented as an add-on of Vanted, a network visualization and analysis tool.

While visual saliency has recently been studied in 3D, the experimental setup for collecting 3D saliency data can be expensive and cumbersome. To address this challenge, we propose a novel experimental design that utilises an eye tracker on a screen to collect 3D saliency data, which could reduce the cost and complexity of data collection. We first collected gaze data on a computer screen and then mapped the 2D points to 3D saliency data through perspective transformation. Using this method, we propose Saliency3D, a 3D saliency dataset (49,276 fixations) comprising 10 participants looking at sixteen objects. We examined the viewing preferences for objects and our results indicate potential preferred viewing directions and a correlation between salient features and the variation in viewing directions.

MOTIVATION: High-throughput omics methods increasingly result in large datasets including metabolomics data, which are often difficult to analyse. RESULTS: To help researchers to handle and analyse those datasets by mapping and investigating metabolomics data of multiple sampling conditions (e.g. different time points or treatments) in the context of pathways, PathwayNexus has been developed, which presents the mapping results in a matrix format, allowing users to easily observe the relations between the compounds and the pathways. It also offers functionalities like ranking, sorting, clustering, pathway views, and further analytical tools. Its primary objective is to condense large sets of pathways into smaller, more relevant subsets that align with the specific interests of the user. AVAILABILITY AND IMPLEMENTATION: The methodology presented here is implemented in PathwayNexus, an open-source add-on for Vanted available at www.cls.uni-konstanz.de/software/pathway-nexus. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Website: www.cls.uni-konstanz.de/software/pathway-nexus.

Data in behavioral research is often quantified with event-logging software, generating large data sets containing detailed information about subjects, recipients, and the duration of behaviors. Exploring and analyzing such large data sets can be challenging without tools to visualize behavioral interactions between individuals or transitions between behavioral states, yet software that can adequately visualize complex behavioral data sets is rare. TIBA (The Interactive Behavior Analyzer) is a web application for behavioral data visualization, which provides a series of interactive visualizations, including the temporal occurrences of behavioral events, the number and direction of interactions between individuals, the behavioral transitions and their respective transitional frequencies, as well as the visual and algorithmic comparison of the latter across data sets. It can therefore be applied to visualize behavior across individuals, species, or contexts. Several filtering options (selection of behaviors and individuals) together with options to set node and edge properties (in the network drawings) allow for interactive customization of the output drawings, which can also be downloaded afterwards. TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. The web application and usage instructions are available at tiba.inf.uni-konstanz.de. The source code is publicly available on GitHub: github.com/LSI-UniKonstanz/tiba.

Relational information between different types of entities is often modelled by a multilayer network (MLN) - a network with subnetworks represented by layers. The layers of an MLN can be arranged in different ways in a visual representation, however, the impact of the arrangement on the readability of the network is an open question. Therefore, we studied this impact for several commonly occurring tasks related to MLN analysis. Additionally, layer arrangements with a dimensionality beyond 2D, which are common in this scenario, motivate the use of stereoscopic displays. We ran a human subject study utilising a Virtual Reality headset to evaluate 2D, 2.5D, and 3D layer arrangements. The study employs six analysis tasks that cover the spectrum of an MLN task taxonomy, from path finding and pattern identification to comparisons between and across layers. We found no clear overall winner. However, we explore the task-to-arrangement space and derive empirical-based recommendations on the effective use of 2D, 2.5D, and 3D layer arrangements for MLNs.

The visual exploration of trajectory data is crucial in domains such as animal behavior, molecular dynamics, and transportation. With the emergence of immersive technology, trajectory data, which is often inherently three-dimensional, can be analyzed in stereoscopic 3D, providing new opportunities for perception, engagement, and understanding. However, the interaction with the presented data remains a key challenge. While most applications depend on hand tracking, we see eye tracking as a promising yet under-explored interaction modality, while challenges such as imprecision or inadvertently triggered actions need to be addressed. In this work, we explore the potential of eye gaze interaction for the visual exploration of trajectory data within an AR environment. We integrate hand- and eye-based interaction techniques specifically designed for three common use cases and address known eye tracking challenges. We refine our techniques and setup based on a pilot user study (n=6) and find in a follow-up study (n=20) that gaze interaction can compete with hand-tracked interaction regarding effectiveness, efficiency, and task load for selection and cluster exploration tasks. However, time step analysis comes with higher answer times and task load. In general, we find the results and preferences to be user-dependent. Our work contributes to the field of immersive data exploration, underscoring the need for continued research on eye tracking interaction.

Abstract After a long period of scepticism, more and more publications describe basic research but also practical approaches to how abstract data can be presented in immersive environments for effective and efficient data understanding. Central aspects of this important research question in immersive analytics research are concerned with the use of 3D for visualization, the embedding in the immersive space, the combination with spatial data, suitable interaction paradigms and the evaluation of use cases. We provide a characterization that facilitates the comparison and categorization of published works and present a survey of publications that gives an overview of the state of the art, current trends, and gaps and challenges in current research.

Networks are an important means for the representation and analysis of data in a variety of research and application areas. While there are many efficient methods to create layouts for networks to support their visual analysis, approaches for the comparison of networks are still underexplored. Especially when it comes to the comparison of weighted networks, which is an important task in several areas, such as biology and biomedicine, there is a lack of efficient visualization approaches. With the availability of affordable high-quality virtual reality (VR) devices, such as head-mounted displays (HMDs), the research field of immersive analytics emerged and showed great potential for using the new technology for visual data exploration. However, the use of immersive technology for the comparison of networks is still underexplored. With this work, we explore how weighted networks can be visually compared in an immersive VR environment and investigate how visual representations can benefit from the extended 3D design space. For this purpose, we develop different encodings for 3D node-link diagrams supporting the visualization of two networks within a single representation and evaluate them in a pilot user study. We incorporate the results into a more extensive user study comparing node-link representations with matrix representations encoding two networks simultaneously. The data and tasks designed for our experiments are similar to those occurring in real-world scenarios. Our evaluation shows significantly better results for the node-link representations, which is contrary to comparable 2D experiments and indicates a high potential for using VR for the visual comparison of networks.

MOTIVATION: Large metabolic models, including genome-scale metabolic models, are nowadays common in systems biology, biotechnology and pharmacology. They typically contain thousands of metabolites and reactions and therefore methods for their automatic visualization and interactive exploration can facilitate a better understanding of these models. RESULTS: We developed a novel method for the visual exploration of large metabolic models and implemented it in LMME (Large Metabolic Model Explorer), an add-on for the biological network analysis tool VANTED. The underlying idea of our method is to analyze a large model as follows. Starting from a decomposition into several subsystems, relationships between these subsystems are identified and an overview is computed and visualized. From this overview, detailed subviews may be constructed and visualized in order to explore subsystems and relationships in greater detail. Decompositions may either be predefined or computed, using built-in or self-implemented methods. Realized as add-on for VANTED, LMME is embedded in a domain-specific environment, allowing for further related analysis at any stage during the exploration. We describe the method, provide a use case and discuss the strengths and weaknesses of different decomposition methods. AVAILABILITY AND IMPLEMENTATION: The methods and algorithms presented here are implemented in LMME, an open-source add-on for VANTED. LMME can be downloaded from www.cls.uni-konstanz.de/software/lmme and VANTED can be downloaded from www.vanted.org. The source code of LMME is available from GitHub, at https://github.com/LSI-UniKonstanz/lmme.

The visualization of a network influences the quality of the mental map that the viewer develops to understand the network. In this study, we investigate the effects of a 3D immersive visualization environment compared to a traditional 2D desktop environment on the comprehension of a network’s structure. We compare the two visualization environments using three tasks—interpreting network structure, memorizing a set of nodes, and identifying the structural changes—commonly used for evaluating the quality of a mental map in network visualization. The results show that participants were able to interpret network structure more accurately when viewing the network in an immersive environment, particularly for larger networks. However, we found that 2D visualizations performed better than immersive visualization for tasks that required spatial memory.

Analysing and understanding brain function and disorder is the main focus of neuroscience. Due to the high complexity of the brain, directionality of the signal and changing activity over time, visual exploration and data analysis are difficult. For this reason, a vast amount of research challenges are still unsolved. We explored different challenges of the visual analysis of brain data and the design of corresponding immersive environments in collaboration with experts from the biomedical domain. We built a prototype of an immersive virtual reality environment to explore the design space and to investigate how brain data analysis can be supported by a variety of design choices. Our environment can be used to study the effect of different visualisations and combinations of brain data representation, as for example network layouts, anatomical mapping or time series. As a long-term goal, we aim to aid neuro-scientists in a better understanding of brain function and disorder.

The recent availability of affordable and lightweight tracking sensors allows researchers to collect large and complex movement datasets. These datasets require applications that are capable of handling them whilst providing an environment that enables the analyst(s) to focus on the task of analysing the movement in the context of the geographic environment it occurred in. We present a framework for collaborative analysis of geospatial-temporal movement data with a use-case in collective behavior analysis. It supports the concurrent usage of several program instances, allowing to have different perspectives on the same data in collocated or remote setups. The implementation can be deployed in a variety of immersive environments, e.g. on a tiled display wall or mobile VR devices.

Recent advances in tracking technology allow biologists to collect large amounts of movement data for a variety of species. Analysis of the collected data supports research on animal behaviour, influence of impact factors such as climate change and human intervention, as well as conservation programs. Analysis of the movement data is difficult, due to the nature of the research questions and the complexity of the data sets. It requires both automated analysis, e.g. for the detection of behavioural patterns, and human inspection, e.g. for interpretation, inclusion of previous knowledge, and for conclusions on future actions and decision making. We present a concept and implementation for the visual analysis of cheetah movement data in a web-based fashion that allows usage both in the field and in office environments.

Chlamydomonas reinhardtii cells have been in the focus of research for more than a decade, in particular due to its use as alternative source for energy production. However, the molecular processes in these cells are still not completely known, and 3D visualisations may help to understand these complex interactions and processes. In previous work, we presented the stereoscopic 3D (S3D) visualisation of a complete Chlamydomonas reinhardtii cell created with the 3D modelling framework Blender. This animation contained already a scene showing an illustrative membrane model of the thylakoid membrane. During discussion with domain experts, shortcomings of the visualisation for several detailed analysis questions have been identified and it was decided to redefine it.

We propose Graph Thumbnails, small icon-like visualisations of the high-level structure of network data. Graph Thumbnails are designed to be legible in small multiples to support rapid browsing within large graph corpora. Compared to existing graph-visualisation techniques our representation has several advantages: (1) the visualisation can be computed in linear time; (2) it is canonical in the sense that isomorphic graphs will always have identical thumbnails; and (3) it provides precise information about the graph structure. We report the results of two user studies. The first study compares Graph Thumbnails to node-link and matrix views for identifying similar graphs. The second study investigates the comprehensibility of the different representations. We demonstrate the usefulness of this representation for summarising the evolution of protein-protein interaction networks across a range of species.

For decades, researchers in information visualisation and graph drawing have focused on developing techniques for the layout and display of very large and complex networks. Experiments involving human participants have also explored the readability of different styles of layout and representations for such networks. In both bodies of literature, networks are frequently referred to as being ‘large’ or ‘complex’, yet these terms are relative. From a human-centred, experiment point-of-view, what constitutes ‘large’ (for example) depends on several factors, such as data complexity, visual complexity, and the technology used. In this paper, we survey the literature on human-centred experiments to understand how, in practice, different features and characteristics of node–link diagrams affect visual complexity.

Functional magnetic resonance imaging (fMRI) is a 4D medical imaging modality that depicts a proxy of neuronal activity in a series of temporal scans. Statistical processing of the modality shows promise in uncovering insights about the functioning of the brain, such as the default mode network, and characteristics of mental disorders. Current statistical processing generally summarises the temporal signals between brain regions into a single data point to represent the 'coactivation' of the regions. That is, how similar are their temporal patterns over the scans. However, the potential of such processing is limited by issues of possible data misrepresentation due to uncertainties, e.g. noise in the data. Moreover, it has been shown that brain signals are characterised by brief traces of coactivation, which are lost in the single value representations. To alleviate the issues, alternate statistical processes have been used, however creating effective techniques has proven difficult due to problems, e.g. issues with noise, which often require user input to uncover. Visual analytics, therefore, through its ability to interactively exploit human expertise, presents itself as an interesting approach of benefit to the domain. In this work, we present the conceptual design behind TemporalTracks, our visual analytics system for exploration of 4D fMRI time-series coactivation data, utilising a visual metaphor to effectively present coactivation data for easier understanding. We describe our design with a case study visually analysing Human Connectome Project data, demonstrating that TemporalTracks can uncover temporal events that would otherwise be hidden in standard analysis.

High-quality immersive display technologies are becoming mainstream with the release of head-mounted displays (HMDs) such as the Oculus Rift. These devices potentially represent an affordable alternative to the more traditional, centralised CAVE-style immersive environments. One driver for the development of CAVE-style immersive environments has been collaborative sense-making. Despite this, there has been little research on the effectiveness of collaborative visualisation in CAVE-style facilities, especially with respect to abstract data visualisation tasks. Indeed, very few studies have focused on the use of these displays to explore and analyse abstract data such as networks and there have been no formal user studies investigating collaborative visualisation of abstract data in immersive environments. In this paper we present the results of the first such study. It explores the relative merits of HMD and CAVE-style immersive environments for collaborative analysis of network connectivity, a common and important task involving abstract data. We find significant differences between the two conditions in task completion time and the physical movements of the participants within the space: participants using the HMD were faster while the CAVE2 condition introduced an asymmetry in movement between collaborators. Otherwise, affordances for collaborative data analysis offered by the low-cost HMD condition were not found to be different for accuracy and communication with the CAVE2. These results are notable, given that the latest HMDs will soon be accessible (in terms of cost and potentially ubiquity) to a massive audience.

Prior research into network layout has focused on fast heuristic techniques for layout of large networks, or complex multi-stage pipelines for higher quality layout of small graphs. Improvements to these pipeline techniques, especially for orthogonal-style layout, are difficult and practical results have been slight in recent years. Yet, as discussed in this paper, there remain significant issues in the quality of the layouts produced by these techniques, even for quite small networks. This is especially true when layout with additional grouping constraints is required. The first contribution of this paper is to investigate an ultra-compact, grid-like network layout aesthetic that is motivated by the grid arrangements that are used almost universally by designers in typographical layout. Since the time when these heuristic and pipeline-based graph-layout methods were conceived, generic technologies (MIP, CP and SAT) for solving combinatorial and mixed-integer optimization problems have improved massively. The second contribution of this paper is to reassess whether these techniques can be used for high-quality layout of small graphs. While they are fast enough for graphs of up to 50 nodes we found these methods do not scale up. Our third contribution is a large-neighborhood search meta-heuristic approach that is scalable to larger networks.

In this work, we introduce SentiCompass for exploring and comparing the sentiments of time-varying Twitter data. Our visualization design combines 2D psychology model of affect (i.e. emotion) with a time tunnel representation. To illustrate our visualization design, two case studies are conducted. They demonstrate the effectiveness of SentiCompass in achieving various tasks related to temporal sentiment and affective analysis of tweets. The interactive demo of our system is available at: http://youtu.be/ZaMF6VNO7tA

In a variety of research and application areas graphs are an important structure for data modeling and analysis. While graph properties can have a crucial influence on the performance of graph algorithms, and thus on the outcome of experiments, often only basic analysis of the graphs under investigation in an experimental evaluation is performed, and a few characteristics are reported in publications.

Content-based image retrieval (CBIR) is a search technique based on the similarity of visual features and has demonstrated potential benefits for medical diagnosis, education, and research. However, clinical adoption of CBIR is partially hindered by the difference between the computed image similarity and the user's search intent, the semantic gap, with the end result that relevant images with outlier features may not be retrieved. Furthermore, most CBIR algorithms do not provide intuitive explanations as to why the retrieved images were considered similar to the query (e.g., which subset of features were similar), hence, it is difficult for users to verify if relevant images, with a small subset of outlier features, were missed. Users, therefore, resort to examining irrelevant images and there are limited opportunities to discover these "missed" images. In this paper, we propose a new approach to medical CBIR by enabling a guided visual exploration of the search space through a tool, called visual analytics for medical image retrieval (VAMIR). The visual analytics approach facilitates interactive exploration of the entire dataset using the query image as a point-of-reference. We conducted a user study and several case studies to demonstrate the capabilities of VAMIR in the retrieval of computed tomography images and multimodality positron emission tomography and computed tomography images.

Efficient subgraph queries in large databases are a time-critical task in many application areas as e.g. biology or chemistry, where biological networks or chemical compounds are modeled as graphs. The NP-completeness of the underlying subgraph isomorphism problem renders an exact subgraph test for each database graph infeasible. Therefore efficient methods have to be found that avoid most of these tests but still allow to identify all graphs containing the query pattern. We propose a new approach based on the filter-verification paradigm, using a new hash-key fingerprint technique with a combination of tree and cycle features for filtering and a new subgraph isomorphism test for verification. Our approach is able to cope with edge and vertex labels and also allows to use wild card patterns for the search. We present an experimental comparison of our approach with state-of-the-art methods using a benchmark set of both real world and generated graph instances that shows its practicability. Our approach is implemented as part of the Scaffold Hunter software, a tool for the visual analysis of chemical compound databases.