  This short paper examines diagrams describing neural network systems in
academic conference proceedings. Many aspects of scholarly communication are
controlled, particularly with relation to text and formatting, but often
diagrams are not centrally curated beyond a peer review. Using a corpus-based
approach, we argue that the heterogeneous diagrammatic notations used for
neural network systems has implications for signification in this domain. We
divide this into (i) what content is being represented and (ii) how relations
are encoded. Using a novel structuralist framework, we use a corpus analysis to
quantitatively cluster diagrams according to the author's representational
choices. This quantitative diagram classification in a heterogeneous domain may
provide a foundation for further analysis.
