Structuralist analysis for neural network system diagrams
This work addresses the issue of inconsistent diagrammatic signification in scholarly communication for researchers in the neural network domain, but it is incremental as it focuses on classification without direct application.
The paper tackled the problem of heterogeneous diagrammatic notations in neural network system diagrams by applying a structuralist framework to quantitatively cluster diagrams based on content representation and relational encoding, providing a foundation for further analysis.
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.