  Depending on the node ordering, an adjacency matrix can highlight distinct
characteristics of a graph. Deriving a "proper" node ordering is thus a
critical step in visualizing a graph as an adjacency matrix. Users often try
multiple matrix reorderings using different methods until they find one that
meets the analysis goal. However, this trial-and-error approach is laborious
and disorganized, which is especially challenging for novices. This paper
presents a technique that enables users to effortlessly find a matrix
reordering they want. Specifically, we design a generative model that learns a
latent space of diverse matrix reorderings of the given graph. We also
construct an intuitive user interface from the learned latent space by creating
a map of various matrix reorderings. We demonstrate our approach through
quantitative and qualitative evaluations of the generated reorderings and
learned latent spaces. The results show that our model is capable of learning a
latent space of diverse matrix reorderings. Most existing research in this area
generally focused on developing algorithms that can compute "better" matrix
reorderings for particular circumstances. This paper introduces a fundamentally
new approach to matrix visualization of a graph, where a machine learning model
learns to generate diverse matrix reorderings of a graph.
