Do Graph Drawing Aesthetics Matter for AI? A Replication of Foundational Studies in Graph Readability

Abstract

Graph drawing aesthetics have traditionally been optimized for human readers, leading to well-established principles such as reducing edge crossings, enhancing symmetry, and minimizing bends. These criteria shape layout algorithms and define what “readability” means in network visualization. Today, however, visualizations are increasingly interpreted not only by humans but also by Large Language Models, which now routinely process scientific papers, blog posts, and figures found online and in the wild. This raises a fundamental question: do the same aesthetic criteria that benefit humans also support AI-based visual understanding? Exploring these criteria would inform us, visualization designers and researchers, on how to create network visualizations that are fit for both human and AI readers, and, in turn, enable AI to navigate visualizations in the wild properly. In order to study these criteria, we replicated two foundational studies that established graph drawing aesthetics for humans—only, in our case, the “participant” is AI, and not human. Our findings indicate that AI and human preferences for network visualization are not necessarily the same. We offer a first glance at AI-specific readability criteria, and point toward visualization guidelines that support both human and machine interpretation.