  Figures are an important channel for scientific communication, used to
express complex ideas, models and data in ways that words cannot. However, this
visual information is mostly ignored in analyses of the scientific literature.
In this paper, we demonstrate the utility of using scientific figures as
markers of knowledge domains in science, which can be used for classification,
recommender systems, and studies of scientific information exchange. We encode
sets of images into a visual signature, then use distances between these
signatures to understand how patterns of visual communication compare with
patterns of jargon and citation structures. We find that figures can be as
effective for differentiating communities of practice as text or citation
patterns. We then consider where these metrics disagree to understand how
different disciplines use visualization to express ideas. Finally, we further
consider how specific figure types propagate through the literature, suggesting
a new mechanism for understanding the flow of ideas apart from conventional
channels of text and citations. Our ultimate aim is to better leverage these
information-dense objects to improve scientific communication across
disciplinary boundaries.
