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These criteria shape layout algorithms and define what \u201creadability\u201d 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\u2010based 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\u2014only, in our case, the \u201cparticipant\u201d is AI, and not human. 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