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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.

Immersive Analytics is concerned with the systematic examination of the benefits and challenges of using immersive environments for data analysis, and the development of corresponding designs that improve the quality and efficiency of the analysis process. While immersive technologies are now broadly available, practical solutions haven’t received broad acceptance in real-world applications outside of several core areas, and proper guidelines on the design of such solutions are still under development. Both fundamental research and applications bring together topics and questions from several fields, and open a wide range of directions regarding underlying theory, evidence from user studies, and practical solutions tailored towards the requirements of application areas. We give an overview on the concepts, topics, research questions, and challenges.

The analysis of movement trajectories plays a central role in many application areas, such as traffic management, sports analysis, and collective behavior research, where large and complex trajectory data sets are routinely collected these days. While automated analysis methods are available to extract characteristics of trajectories such as statistics on the geometry, movement patterns, and locations that might be associated with important events, human inspection is still required to interpret the results, derive parameters for the analysis, compare trajectories and patterns, and to further interpret the impact factors that influence trajectory shapes and their underlying movement processes. Every step in the acquisition and analysis pipeline might introduce artifacts or alterate trajectory features, which might bias the human interpretation or confound the automated analysis. Thus, visualization methods as well as the visualizations themselves need to take into account the corresponding factors in order to allow sound interpretation without adding or removing important trajectory features or putting a large strain on the analyst. In this paper, we provide an overview of the challenges arising in robust trajectory visualization tasks. We then discuss several methods that contribute to improved visualizations. In particular, we present practical algorithms for simplifying trajectory sets that take semantic and uncertainty information directly into account. Furthermore, we describe a complementary approach that allows to visualize the uncertainty along with the trajectories.

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.

Parallel coordinate plots (PCPs) are among the most useful techniques for the visualization and exploration of high-dimensional data spaces. They are especially useful for the representation of correlations among the dimensions, which identify relationships and interdependencies between variables. However, within these high-dimensional spaces, PCPs face difficulties in displaying the correlation between combinations of dimensions and generally require additional display space as the number of dimensions increases. In this paper, we present a new technique for high-dimensional data visualization in which a set of low-dimensional PCPs are interactively constructed by sampling user-selected subsets of the high-dimensional data space. In our technique, we first construct a graph visualization of sets of well-correlated dimensions. Users observe this graph and are able to interactively select the dimensions by sampling from its cliques, thereby dynamically specifying the most relevant lower dimensional data to be used for the construction of focused PCPs. Our interactive sampling overcomes the shortcomings of the PCPs by enabling the visualization of the most meaningful dimensions (i.e., the most relevant information) from high-dimensional spaces. We demonstrate the effectiveness of our technique through two case studies, where we show that the proposed interactive low-dimensional space constructions were pivotal for visualizing the high-dimensional data and discovering new patterns.

Analysis of functional magnetic resonance imaging (fMRI) plays a pivotal role in uncovering an understanding of the brain. fMRI data contain both spatial volume and temporal signal information, which provide a depiction of brain activity. The analysis pipeline, however, is hampered by numerous uncertainties in many of the steps; often seen as one of the last hurdles for the domain. In this review, we categorise fMRI research into three pipeline phases: (i) image acquisition and processing; (ii) image analysis; and (iii) visualisation and human interpretation, to explore the uncertainties that arise in each phase, including the compound effects due to the inter-dependence of steps. Attempts at mitigating uncertainties rely on providing interactive visual analytics that aid users in understanding the effects of the uncertainties and adjusting their analyses. This impetus for visual analytics comes in light of considerable research investigating uncertainty throughout the pipeline. However, to the best of our knowledge, there is yet to be a comprehensive review on the importance and utility of uncertainty visual analytics (UVA) in addressing fMRI concerns, which we term fMRI-UVA. Such techniques have been broadly implemented in related biomedical fields, and its potential for fMRI has recently been explored; however, these attempts are limited in their scope and utility, primarily focussing on addressing small parts of single pipeline phases. Our comprehensive review of the fMRI uncertainties from the perspective of visual analytics addresses the three identified phases in the pipeline. We also discuss the two interrelated approaches for future research opportunities for fMRI-UVA.

Network analytics provides a scientific solution to interact with networks through effective management of network data and corresponding visualization. Network analytics has been considered essential in many research areas as well as in commercial applications, including use cases in bioinformatics, biomedicine, security, social science, and knowledge graph exploration. We see that networks and the associated analysis process are expected to be initially supported by graph databases with advanced query engines, and later the filtered contents are rendered using corresponding visualization approaches for better communication and knowledge exchange. For this special issue (SI), we recruited papers describing research based on graph database utilization and network visualization that potentially could be integrated as a full pipeline, as well as their synergy for various applied problems. Through the formal IEEE Computer Graphics and Applications review process of the four submissions, we accepted three papers for this SI, focusing on the novelty and potential integration within the network analytics pipeline. These selected papers demonstrate the wide potentiality of techniques for complex network analytics.

The growing interest in chemogenomics approaches over the last years has led to an increasing amount of data regarding chemical and the corresponding biological activity space. The resulting data, collected in either in‐house or public databases, need to be analyzed efficiently to speed‐up the increasingly difficult task of drug discovery. Unfortunately, the discovery of new chemical entities or new targets for known drugs (‘drug repurposing’) is not suitable to a fully automated analysis or a simple drill down process. Visual interactive interfaces that allow to explore chemical space in a systematic manner and facilitate analytical reasoning can help to overcome these problems. Scaffold Hunter is a tool for the visual analysis of chemical compound databases that provides integrated visualization and analysis of biological activity data and fosters the interactive exploration of data imported from a variety of sources. We describe the features and illustrate the use by means of an exemplary analysis workflow.

Unified modelling language (UML) diagrams have become increasingly important in engineering and re-engineering processes for software systems. Of particular interest are UML class diagrams whose purpose is to display generalizations, associations, aggregations, and compositions in one picture. The combination of directed and undirected relations poses a special challenge to a graph layout tool. Current approaches for the automatic layout of class diagrams are based on the layered graph drawing paradigm. These algorithms produce good results for class diagrams with large and deep structural information, that is, diagrams with a large and deep inheritance hierarchy. However, they do not perform satisfactorily in absence of this information. We suggest to use the topology-shape—metrics paradigm for automatic layout of class diagrams, which has been used very successfully for drawing undirected graphs in orthogonal style. Moreover, we introduce the algorithms UML-Kandinsky and GoVisual fitting into this paradigm. Both algorithms work for class diagrams with rich structural information as well as for class diagrams with few or no structural information. Therefore, they improve the existing algorithms significantly.

Understanding the movement of animals is important for a wide range of scientific interests including migration, disease spread, collective movement behaviour and analysing motion in relation to dynamic changes of the environment such as wind and thermal lifts. Particularly, the three-dimensional (3D) spatial–temporal nature of bird movement data, which is widely available with high temporal and spatial resolution at large volumes, presents a natural option to explore the potential of immersive analytics (IA). We investigate the requirements and benefits of a wide range of immersive environments for explorative visualization and analytics of 3D movement data, in particular regarding design considerations for such 3D immersive environments, and present prototypes for IA solutions. Tailored to biologists studying bird movement data, the immersive solutions enable geo-locational time-series data to be investigated interactively, thus enabling experts to visually explore interesting angles of a flock and its behaviour in the context of the environment. The 3D virtual world presents the audience with engaging and interactive content, allowing users to ‘fly with the flock’, with the potential to ascertain an intuitive overview of often complex datasets, and to provide the opportunity thereby to formulate and at least qualitatively assess hypotheses. This work also contributes to ongoing research efforts to promote better understanding of bird migration and the associated environmental factors at the global scale, thereby providing a visual vehicle for driving public awareness of environmental issues and bird migration patterns.