  Content recommendation tasks increasingly use Graph Neural Networks, but it
remains challenging for machine learning experts to assess the quality of their
outputs. Visualization systems for GNNs that could support this interrogation
are few. Moreover, those that do exist focus primarily on exposing GNN
architectures for tuning and prediction tasks and do not address the challenges
of recommendation tasks. We developed RekomGNN, a visual analytics system that
supports ML experts in exploring GNN recommendations across several dimensions
and making annotations about their quality. RekomGNN straddles the design space
between Neural Network and recommender system visualization to arrive at a set
of encoding and interaction choices for recommendation tasks. We found that
RekomGNN helps experts make qualitative assessments of the GNN's results, which
they can use for model refinement. Overall, our contributions and findings add
to the growing understanding of visualizing GNNs for increasingly complex
tasks.
