  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.
