Large-scale and objective painting analyses have recently gained attention. In particular, analyzing influence between individual painters requires substantial effort and is hard to reproduce due to subjectivity. Despite increasing demand for automatic estimation, this remains unresolved because such influence is complex and often directional, making it difficult to model. In this paper, we develop an interactive system that visualizes, manipulates, and analyses chains of painterly influence as a network. Using 32,401 paintings, the system infers directional links from color and brushstroke features. The resulting network based on color style features captures stylistic lineages such as landscape-focused and portrait-focused streams, while a multifaceted analysis of Picasso shows that Cézanne’s impact appears in brushwork rather than color. Our contributions are twofold: (1) the use of an evolutionary model to assign explicit direction to painter influence and support art historical interpretation, and (2) providing a visualization system that allows dynamic comparison of influence networks based on multiple image features.

Biological networks describe complex relationships in biological systems, which represent biological entities as vertices and their underlying connectivity as edges. Ideally, for a complete analysis of such systems, domain experts need to visually integrate multiple sources of heterogeneous data , and visually, as well as numerically, probe said data in order to explore or validate (mechanistic) hypotheses. Such visual analyses require the coming together of biological domain experts, bioinformaticians, as well as network scientists to create useful visualization tools. Owing to the underlying graph data becoming ever larger and more complex, the visual representation of such biological networks has become challenging in its own right. This introduction and survey aims to describe the current state of biological network visualization in order to identify scientific gaps for visualization experts, network scientists, bioinformaticians, and domain experts, such as biologists, or biochemists, alike. Specifically, we revisit the classic visualization pipeline, upon which we base this paper’s taxonomy and structure, which in turn forms the basis of our literature classification. This pipeline describes the process of visualizing data, starting with the raw data itself, through the construction of data tables, to the actual creation of visual structures and views, as a function of task-driven user interaction. Literature was systematically surveyed using API-driven querying where possible, and the collected papers were manually read and categorized based on the identified sub-components of this visualization pipeline’s individual steps. From this survey, we highlight a number of exemplary visualization tools from multiple biological sub-domains in order to explore how they adapt these discussed techniques and why. Additionally, this taxonomic classification of the collected set of papers allows us to identify existing gaps in biological network visualization practices. We finally conclude this report with a list of open challenges and potential research directions. Examples of such gaps include (i) the overabundance of visualization tools using schematic or straight-line node-link diagrams, despite the availability of powerful alternatives, or (ii) the lack of visualization tools that also integrate more advanced network analysis techniques beyond basic graph descriptive statistics.

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 User confidence plays an important role in guided visual data analysis scenarios, especially when uncertainty is involved in the analytical process. However, measuring confidence in practical scenarios remains an open challenge, as previous work relies primarily on self‐reporting methods. In this work, we propose a quantitative approach to measure user confidence—as opposed to trust—in an analytical scenario. We do so by exploiting the respective user interaction provenance graph and examining the impact of guidance using a set of network metrics. We assess the usefulness of our proposed metrics through a user study that correlates results obtained from self‐reported confidence assessments and our metrics—both with and without guidance. The results suggest that our metrics improve the evaluation of user confidence compared to available approaches. In particular, we found a correlation between self‐reported confidence and some of the proposed provenance network metrics. The quantitative results, though, do not show a statistically significant impact of the guidance on user confidence. An additional descriptive analysis suggests that guidance could impact users' confidence and that the qualitative analysis of the provenance network topology can provide a comprehensive view of changes in user confidence. Our results indicate that our proposed metrics and the provenance network graph representation support the evaluation of user confidence and, subsequently, the effective development of guidance in VA.

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/ .

Observation of precipitation changes is important for a variety of purposes such as predicting river levels. Previous studies for data visualization of precipitation and river water levels plotted graphs and color bars were many stations on a map. Instead of such visualizations on a map, we construct a graph to imitate a connected structure such as a tributary of a river in this study. Our method displays two pseudo-coloring sparklines at nodes of the graph as the stations. The method can visualize the time difference between the increase in precipitation upstream and the increase in river water level downstream. Users can observe precipitation and river water levels at different observation points. Our method uses a Delaunay diagram connecting gauging positions to interpolate and calculate precipitation at river level observation points. This avoids the discrepancy between observation points.In addition, we adjust the amount of visualized information by skipping the display of several observation points based on the similarity of the time-series data at each station, which is calculated by applying the dynamic time-stretching method. The visualization results show that downstream, once the water level rises, it tends to take longer for the water level to drop. In addition, the results show that a time lag occurs between the increase in precipitation and the rise in river levels in the mainstream, while tributaries have little time lag. In addition, data on rainfall and river levels at the same station over multiple periods and their relationship are plotted as scatter plots. The scatter plots make it easier to compare data from multiple periods at the same time than two-tone pseudo coloring sparklines.

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.

Abstract We conducted a study to systematically investigate the communication of complex dynamic processes along a two‐dimensional design space, where the axes represent a representation's manifestation (physical or virtual) and operation (manual or automatic). We exemplify the design space on a model embodying cardiovascular pathologies, represented by a mechanism where a liquid is pumped into a draining vessel, with complications illustrated through modifications to the model. The results of a mixed‐methods lab study with 28 participants show that both physical manifestation and manual operation have a strong positive impact on the audience's engagement. The study does not show a measurable knowledge increase with respect to cardiovascular pathologies using manually operated physical representations. However, subjectively, participants report a better understanding of the process—mainly through non‐visual cues like haptics, but also auditory cues. The study also indicates an increased task load when interacting with the process, which, however, seems to play a minor role for the participants. Overall, the study shows a clear potential of physicalization for the communication of complex dynamic processes, which only fully unfold if observers have to chance to interact with the process.

We present a novel vertex-splitting approach with which to iteratively resolve edge crossings in order to improve the readability of graph drawings. Dense graphs, even when small in size (10 to 15 nodes in size) quickly become difficult to read with increasing numbers of edges, and form so-called “hairballs”. The readability of a graph drawing is measured using many different quantitative aesthetic metrics. One such metric of particular importance is the number of edge crossings. Classical approaches to improving readability, such as the minimization of the number of edge crossings, focus on providing overviews of the input graph by aggregating or sampling vertices and/or edges. However, this simplification of the graph drawing does not allow for detailed views into the data, as not all vertices or edges are rendered, and also requires sophisticated interaction approaches to perform well. To avoid this, our locally optimal vertex splitting approach aims to minimize the number of remaining edge crossings while also minimizing the number of vertices that need to be split. In each iteration, we identify the vertex contributing the largest number of edge crossings, remove it, locate the embedding locations of said vertex’s two split copies, and determine each copy’s unique adjacency. We conduct a user study with 52 participants to evaluate whether vertex splitting affects users’ abilities to conduct a set of graph analytical tasks on graphs 12 nodes in size. Users were tasked with identifying a vertex’s adjacency, determining the shared neighbors of two vertices, and checking the validity of a set of paths. We ultimately conclude that within the context of small, dense graphs, systematic vertex splitting is preferred by participants and even positively impacts user performance, though at the cost of the time taken per task.