The Figure 3 .3 has incorrectly updated in the chapter 3 of this book and that has now been corrected.

The emergence of multilayer networks as a concept from the field of complex systems provides many new opportunities for the visualization of network complexity, and has also raised many new exciting challenges. The multilayer network model recognizes that the complexity of relationships between entities in real-world systems is better embraced as several interdependent subsystems (or layers) rather than a simple graph approach. Despite only recently being formalized and defined, this model can be applied to problems in the domains of life sciences, sociology, digital humanities, and more. Within the domain of network visualization there already are many existing systems, which visualize data sets having many characteristics of multilayer networks, and many techniques, which are applicable to their visualization. In this Synthesis Lecture, we provide an overview and structured analysis of contemporary multilayer network visualization. This is not only for researchers in visualization, but also for those who aim to visualize multilayer networks in the domain of complex systems, as well as those solving problems within application domains. We have explored the visualization literature to survey visualization techniques suitable for multilayer network visualization, as well as tools, tasks, and analytic techniques from within application domains. We also identify the research opportunities and examine outstanding challenges for multilayer network visualization along with potential solutions and future research directions for addressing them.

Polyline leaders form a natural generalization of straight-line leaders by supporting bends. To facilitate readability, polyline leaders usually have a schematic and tidy appearance caused by the restriction to segment orientations that are aligned with the Cartesian coordinate axes or possibly with their two bisecting diagonals. As one would expect, the most natural requirement in labelings with polyline leaders is the avoidance of crossings between leaders. However, bend minimization is another criterion which becomes important, as has also been observed in the early survey on boundary labeling by Kaufmann in 2009 [60].

In this book, we explored the design of embodied human-data interactions, i.e., installations that facilitate exploration and sensemaking of large sets of data through embodied control. Because human-data interaction is a relatively recent sub-field in interaction design, however, there are still many exciting opportunities—and challenges—for research in the field. For this reason, we want to conclude with a look forward toward some of the open challenges that highlight promising research directions for HDI.

In this chapter, we narrow down the focus on human-data interaction in museums. The overarching theme is that museum visitors typically spend less than two minutes with an exhibit, even one that they really like (Sandifer [1997]; Ma et. al [2020]). The brevity of these interactions, in combination with the distinctly different—or even absent—motivations for engaging deeply with the data, calls for unique design requirements. We discuss how embodiment and novel, engaging interactions can provide extrinsic motivations to (literally) jump in, and we describe strategies to lure visitors into the interaction with HDI systems.

While embodied interactions for learning have gained some traction in formal school settings (e.g., Enyedy et al. [2013]), non-school institutions such as museums, aquariums, zoos, and science centers (hereafter referred to collectively as “museums”) are prime venues for incorporating movement-based and off-the-desktop interactions. These designed spaces for informal learning are created to engage learners with content and advance disciplinary understanding while presenting enjoyable, novel, and memorable experiences to visitors (National Research Council [2009]). Moreover, museums are increasingly attending to ways of presenting authentic data to visitors (Ma et al. [2019], J. Roberts and Lyons [2020]): in a world in which people are bombarded with data every time they surf the Internet, read newspapers, or listen to debates on TV, data are increasingly seen as “artifacts” worthy to be on display in museums. Thus, museums present an ideal setting for our exploration of embodied HDI, and we therefore focus on museums through the remainder of this book.