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Network visualization is one of the most widely used tools in digital humanities research. The idea of uncertain or “fuzzy” data is also a core notion in digital humanities research. Yet network visualizations in digital humanities do not always prominently represent uncertainty. In this article, we present a mathematical and logical model of uncertainty as a range of values which can be used in network visualizations. We review some of the principles for visualizing uncertainty of different kinds, visual variables that can be used for representing uncertainty, and how these variables have been used to represent different data types in visualizations drawn from a range of non-humanities fields like climate science and bioinformatics. We then provide examples of two diagrams: one in which the variables displaying degrees of uncertainty are integrated/pinto the graph and one in which glyphs are added to represent data certainty and uncertainty. Finally, we discuss how probabilistic data and what-if scenarios could be used to expand the representation of uncertainty in humanities network visualizations.

This paper presents OpMAP: a tool for visualizing large scale, multi-dimensional opinion spaces as geographic maps. OpMAP represents opinions as labelings on a structured deductive argumentation framework. It uses probabilistic degrees of justification and Bayesian coherence measures to calculate how strongly any two opinions cohere with each other. The opinion sample is, accordingly, represented as a weighted graph, a so-called opinion graph, with opinion vectors serving as nodes and coherence values as edge weights. OpMAP partitions the nodes of the opinion graph by using clustering methods. Finally, the graph is visualized as a geographic map using a method based on a particular (e.g., force-directed) layout.

Positive effects of aesthetically appreciated designs have long been studied and confirmed since the 19th century: such designs are more enjoyable, they are more forgivable for glitches and can increase users’ performance. In the field of information visualization, studies of aesthetics are still a niche approach. In the current study, we aim to specifically understand which parameters in a visualization of node-link diagrams make them aesthetically pleasing-an important extension to already existing research on usability and readability aspects. We investigated how the shape of the outline of such diagrams influences the aesthetic judgments on two of the most important dimensions of aesthetic appeal: beauty and interest. We employed different outlines to node-link diagrams and compared them with uniformly filled shapes, varying two important variables typically impacting aesthetics: complexity and curvature. This was done for a short (100 ms) and ad libitum presentation time. Diagrams with curvier outlines were perceived as more beautiful, while diagrams with more complex outlines were considered to be more interesting. These dependencies already exist for presaccadic perception (100 ms) and are slightly stronger for unlimited presentation time. We also found that curvature is a predictor for beauty only for unlimited presentation time. Aesthetic appeal was very similar for diagrams and pure shapes, so many results from fundamental research on aesthetics can potentially be transferred to the community of network visualization, assisting to improve visualizations also in aesthetic regards.

We study the following problem: Given $k$ paths that share the same vertex set, is there a simultaneous geometric embedding of these paths such that each individual drawing is monotone in some direction? We prove that for any dimension $d\geq 2$, there is a set of $d + 1$ paths that does not admit a monotone simultaneous geometric embedding.