Graph-Convolutional Autoencoder Ensembles for the Humanities, Illustrated with a Study of the American Slave Trade
This work addresses the problem of integrating deep learning into humanities scholarship for researchers in both fields, though it appears incremental as it builds on existing graph-convolutional and autoencoder methods.
The authors tackled the challenge of applying deep learning to humanities research by introducing a graph-aware autoencoder ensemble framework that maintains interpretability and facilitates collaboration between traditional and computational scholars, as demonstrated through a study of the American slave trade and supported by a growing suite of two dozen studies across various fields.
We introduce a graph-aware autoencoder ensemble framework, with associated formalisms and tooling, designed to facilitate deep learning for scholarship in the humanities. By composing sub-architectures to produce a model isomorphic to a humanistic domain we maintain interpretability while providing function signatures for each sub-architectural choice, allowing both traditional and computational researchers to collaborate without disrupting established practices. We illustrate a practical application of our approach to a historical study of the American post-Atlantic slave trade, and make several specific technical contributions: a novel hybrid graph-convolutional autoencoder mechanism, batching policies for common graph topologies, and masking techniques for particular use-cases. The effectiveness of the framework for broadening participation of diverse domains is demonstrated by a growing suite of two dozen studies, both collaborations with humanists and established tasks from machine learning literature, spanning a variety of fields and data modalities. We make performance comparisons of several different architectural choices and conclude with an ambitious list of imminent next steps for this research.