Uncertainty is common to most types of data, from meteorology to the biomedical sciences. Here, we are interested in the visualization of uncertainty within the context of multivariate graphs, specifically the visualization of uncertainty attached to node attributes. Many visual channels offer themselves up for the visualization of node attributes and their uncertainty. One controversial and relatively under-explored channel, however, is animation, despite its conceptual advantages. In this paper, we investigate node “wiggliness”, i.e. uncertainty-dependent pseudo-random motion of nodes, as a potential new visual channel with which to communicate node attribute uncertainty. To study wiggliness’ effectiveness, we compare it against three other visual channels identified from a thorough review of uncertainty visualization literature—namely node enclosure, node fuzziness, and node color saturation. In a larger-scale, mixed method, Prolific -crowd-sourced, online user study of 160 participants, we quantitatively and qualitatively compare these four uncertainty encodings across eight low-level graph analysis tasks that probe participants’ abilities to parse the presented networks both on an attribute and topological level. We ultimately conclude that all four uncertainty encodings appear comparably useful—as opposed to previous findings. Wiggliness may be a suitable and effective visual channel with which to communicate node attribute uncertainty, at least for the kinds of data and tasks considered in our study.

Abstract Physicalizations, which combine perceptual and sensorimotor interactions, offer an immersive way to comprehend complex data visualizations by stimulating active construction and manipulation. This study investigates the impact of personal construction on the comprehension of physicalized networks. We propose a physicalization toolkit— NODKANT —for constructing modular node‐link diagrams consisting of a magnetic surface, 3D printable and stackable node labels, and edges of adjustable length. In a mixed‐methods between‐subject lab study with 27 participants, three groups of people used NODKANT to complete a series of low‐level analysis tasks in the context of an animal contact network. The first group was tasked with freely constructing their network using a sorted edge list, the second group received step‐by‐step instructions to create a predefined layout, and the third group received a pre‐constructed representation. While free construction proved on average more time‐consuming, we show that users extract more insights from the data during construction and interact with their representation more frequently, compared to those presented with step‐by‐step instructions. Interestingly, the increased time demand cannot be measured in users' subjective task load. Finally, our findings indicate that participants who constructed their own representations were able to recall more detailed insights after a period of 10–14 days compared to those who were given a pre‐constructed network physicalization. All materials, data, code for generating instructions, and 3D printable meshes are available on https://osf.io/tk3g5/ .

Abstract Art history traditionally relies on qualitative methods. However, the increasing availability of digitized archives has opened new possibilities for research by integrating visual analytics. This survey presents a comprehensive review of the intersection between art history and visual analytics, focusing on network visualization and how it supports researchers in analyzing and understanding complex art historical relationships through nodes (e.g., artists, artworks, institutions) and edges (the relationships between them). We explore how these approaches enable dynamic analysis, offering novel perspectives on artistic influence, stylistic evolution, and social interactions within the art world. Through this, we also examine wedges, a metaphor for the friction often present in art history between individuals and institutions. These tensions, which have historically played a pivotal role in shaping artistic movements, are now better understood through the lens of network visualization, revealing how conflicts and power dynamics influenced the development of art. Through a hierarchical categorization of the literature, we outline saturated problems and research areas as well as ongoing challenges in art historical research. Furthermore, we highlight the potential of visual analytics to bridge the gap between traditional qualitative research and modern computational analysis, offering interactive exploration, temporal analysis, and complex network visualization. We provide a structured foundation for future research in art history, emphasizing the value of network visualization in enriching the understanding of art history.

In temporal (event-based) networks, time is a continuous axis, with real-valued time coordinates for each node and edge. Computing a layout for such graphs means embedding the node trajectories and edge surfaces over time in a$2D + t$space, known as the space-time cube. Currently, these space-time cube layouts are visualized through animation or by slicing the cube at regular intervals. However, both techniques present problems such as below-average performance on tasks as well as loss of precision and difficulties in selecting timeslice intervals. In this article, we presentTimeLighting, a novel visual analytics approach to visualize and explore temporal graphs embedded in the space-time cube. Our interactive approach highlights node trajectories and their movement over time, visualizes node “aging”, and provides guidance to support users during exploration by indicating interesting time intervals (“when”) and network elements (“where”) are located for a detail-oriented investigation. This combined focus helps to gain deeper insights into the temporal network's underlying behavior. We assess the utility and efficacy of our approach through two case studies and qualitative expert evaluation. The results demonstrate howTimeLightingsupports identifying temporal patterns, extracting insights from nodes with high activity, and guiding the exploration and analysis process.

From social networks to brain connectivity, ego networks are a simple yet powerful approach to visualizing parts of a larger graph, i.e. those related to a selected focal node — the so-called “ego”. While surveys and comparisons of general graph visualization approaches exist in the literature, we note (i) the many conflicting results of comparisons of adjacency matrices and node-link diagrams, thus motivating further study, as well as (ii) the absence of such systematic comparisons for ego networks specifically. In this paper, we propose the development of empirical recommendations for ego network visualization strategies. First, we survey the literature across application domains and collect examples of network visualizations to identify the most common visual encodings, namely straight-line, radial, and layered node-link diagrams, as well as adjacency matrices. These representations are then applied to a representative, intermediate-sized network and subsequently compared in a large-scale, crowd-sourced user study in a mixed-methods analysis setup to investigate their impact on both user experience and performance. Within the limits of this study, and contrary to previous comparative investigations of adjacency matrices and node-link diagrams (outside of ego networks specifically), participants performed systematically worse when using adjacency matrices than those using node-link diagrammatic representations . Similar to previous comparisons of different node-link diagrams, we do not detect any notable differences in participant performance between the three node-link diagrams . Lastly, our quantitative and qualitative results indicate that participants found adjacency matrices harder to learn, use, and understand than node-link diagrams . We conclude that in terms of both participant experience and performance, a layered node-link diagrammatic representation appears to be the most preferable for ego network visualization purposes. • Literature survey of ego network visualization approaches across domains to characterize the current state of the art. • Identification of the most common approaches for ego network visualization. • Online study on the effect of ego network representation on user performance and experience in mixed methods analysis. • Development of recommendations for the effective visualization of ego networks.

The dynamic network visualization design space consists of two major dimensions: network structural and temporal representation. As more techniques are developed and published, a clear need for evaluation and experimental comparisons between them emerges. Most studies explore the temporal dimension and diverse interaction techniques supporting the participants, focusing on a single structural representation. Empirical evidence about performance and preference for different visualization approaches is scattered over different studies, experimental settings, and tasks. This paper aims to comprehensively investigate the dynamic network visualization design space in two evaluations. First, a controlled study assessing participants' response times, accuracy, and preferences for different combinations of network structural and temporal representations on typical dynamic network exploration tasks, with and without the support of standard interaction methods. Second, the best-performing combinations from the first study are enhanced based on participants' feedback and evaluated in a heuristic-based qualitative study with visualization experts on a real-world network. Our results highlight node-link with animation and playback controls as the best-performing combination and the most preferred based on ratings. Matrices achieve similar performance to node-link in the first study but have considerably lower scores in our second evaluation. Similarly, juxtaposition exhibits evident scalability issues in more realistic analysis contexts.

In temporal (or event-based) networks, time is a continuous axis, with real-valued time coordinates for each node and edge. Computing a layout for such graphs means embedding the node trajectories and edge surfaces over time in a $$2D + t$$ space, known as the space-time cube. Currently, these space-time cube layouts are visualized through animation or by slicing the cube at regular intervals. However, both techniques present problems ranging from sub-par performance on some tasks to loss of precision. In this paper, we present TimeLighting, a novel visual analytics approach to visualize and explore temporal graphs embedded in the space-time cube. Our interactive approach highlights the node trajectories and their mobility over time, visualizes node “aging”, and provides guidance to support users during exploration. We evaluate our approach through two case studies, showing the system’s efficacy in identifying temporal patterns and the role of the guidance features in the exploration process.

Networks are abstract and ubiquitous data structures, defined as a set of data points and relationships between them. Network visualization provides meaningful representations of these data, supporting researchers in understanding the connections, gathering insights, and detecting and identifying unexpected patterns. Research in this field is focusing on increasingly challenging problems, such as visualizing dynamic, complex, multivariate, and geospatial networked data. This ever-growing, and widely varied, body of research led to several surveys being published, each covering one or more disciplines of network visualization. Despite this effort, the variety and complexity of this research represents an obstacle when surveying the domain and building a comprehensive overview of the literature. Furthermore, there exists a lack of clarification and uniformity between the terminology used in each of the surveys, which requires further effort when mapping and categorizing the plethora of different visualization techniques and approaches. In this paper, we aim at providing researchers and practitioners alike with a "roadmap" detailing the current research trends in the field of network visualization. We design our contribution as a meta-survey where we discuss, summarize, and categorize recent surveys and task taxonomies published in the context of network visualization. We identify more and less saturated disciplines of research and consolidate the terminology used in the surveyed literature. We also survey the available task taxonomies, providing a comprehensive analysis of their varying support to each network visualization discipline and by establishing and discussing a classification for the individual tasks. With this combined analysis of surveys and task taxonomies, we provide an overarching structure of the field, from which we extrapolate the current state of research and promising directions for future work.

A temporal graph stores and reflects temporal information associated with its entities and relationships. Such graphs can be utilized to model a broad variety of problems in a multitude of domains. Re-searchers from different fields of expertise are increasingly applying graph visualization and analysis to explore unknown phenomena, complex emerging structures, and changes occurring over time in their data. While several empirical studies evaluate the benefits and drawbacks of different network representations, visualizing the temporal dimension in graphs still presents an open challenge. In this paper we propose an exploratory user study with the aim of evaluating different combinations of graph representations, namely node-link and adjacency matrix, and temporal encodings, such as superimposition, juxtaposition and animation, on typical temporal tasks. The study participants expressed positive feedback toward matrix representations, with generally quicker and more accurate responses than with the node-link representation.

In the application domain of digital humanities network visualization is increasingly being used to conduct research as the main interests of the domain experts lie in exploring and analyzing relationships between entities and their changes over time. Visualizing the dynamics and different perspectives of such data is a non-trivial task but it enables researchers to explore connections between disparate entities and investigate historical narratives that emerge. In this paper we present Circular, an interactive exploration environment to visualize event-based networks and support research in digital humanities through visualization of historical subjects in space and time. Our radial design is the result of iterative collaboration with domain experts, and we discuss the process of collaborative development and exploration of public music festivities in Vienna as an example of immersive development methodology. We validate our approach by means of both domain and visualization expert interviews and show the potential of this approach in supporting the visual exploration of historical subjects. We discuss our design rationales, visual encodings, and interactions as to allow the reproducibility of this approach within a framework of transdisciplinary collaboration with digital humanities.

Abstract The Curriculum Vitae (CV, also referred to as “résumé”) is an established representation of a person's academic and professional history. A typical CV is comprised of multiple sections associated with spatio‐temporal, nominal, hierarchical, and ordinal data. The main task of a recruiter is, given a job application with specific requirements, to compare and assess CVs in order to build a short list of promising candidates to interview. Commonly, this is done by viewing CVs in a side‐by‐side fashion. This becomes challenging when comparing more than two CVs, because the reader is required to switch attention between them. Furthermore, there is no guarantee that the CVs are structured similarly, thus making the overview cluttered and significantly slowing down the comparison process. In order to address these challenges, in this paper we propose “CV3”, an interactive exploration environment offering users a new way to explore, assess, and compare multiple CVs, to suggest suitable candidates for specific job requirements. We validate our system by means of domain expert feedback whose results highlight both the efficacy of our approach and its limitations. We learned that CV3 eases the overall burden of recruiters thereby assisting them in the selection process.