This week, we discussed network analysis. Melanie Conroy, in her article “Networks, Maps, and Time: Visualizing Historical Networks Using Palladio”, discusses how a specific technology called Palladio can be used to create networks. Palladio is a tool designed for historians and related disciplines, facilitating the spatial and temporal visualization of data. It was developed by the Humanities + Design Lab, to fulfill the vision for the study of social networks in the humanities. Palladio is suitable for qualitative studies, providing visualizations like maps and network graphs that are familiar to people like art historians and anthropologists. It allows for the presentation of multifaceted data, such as network data with date ranges or categories.
Unlike other network graph packages, Palladio doesn’t have advanced network analytics features but she argues that it does well at presenting historical case studies because it is considered more of a software solution that makes design decisions for users. Palladio was specifically developed for historians, and considers best practices, design research methods, peer critiques, and usability testing. I think it’s really interesting that technology was developed specifically for the humanities! I don’t typically think of humanities being the focus of tech research. The focus is on ease of use, combining diagram types, and quick prototyping.
The tool’s simplicity allows historians to rapidly prototype diagrams, filter data, and explore subsets in highly legible diagrams that can be used for both print and online use. Palladio’s outputs are also not copyrighted, making them suitable for use in commercial and non-commercial works. Even for me, who has been described as a “boomer” for my technology capabilities, found Palladio shockingly easy to use. Unlike other network analysis programs, Palladio’s diagrams are clear and easy to read.
In Houda Lamgaddam, Inez de Prekel, Koenraad Brosens, and Katrien Verbert article, “Perceptual Effects of Hierarchy in Art Historical Social Networks”, the article discusses the impacts of perceived hierarchies in visualizations of historical social networks. Focusing on how the networks are understood, the study finds that human participants tend to have a hierarchical bias when viewing social networks. The hypothesis was that representing social networks in a way that presents it hierarchically would have advantages or sway the audience. This was confirmed and users reported lower cognitive load, more frequent and deeper insights, and a strong preference for hierarchical representations. Despite understanding the meaningful topology in the graphs, they emphasize the importance of considering perceptual benefits in hierarchically structured layouts, particularly in the humanities fields.
The authors encourage an open and critical discussion on the role of network visualization in Humanities research and suggest that this method of structuring layouts should be more commonly accepted as an alternative to graphs. I think the authors are successful in their goal is to provide scholars with different perspectives on their data as a contribution to the ongoing dialogue about the impact that structure can have in network visualization.