  Recent advances in artificial intelligence largely benefit from better neural
network architectures. These architectures are a product of a costly process of
trial-and-error. To ease this process, we develop ArchExplorer, a visual
analysis method for understanding a neural architecture space and summarizing
design principles. The key idea behind our method is to make the architecture
space explainable by exploiting structural distances between architectures. We
formulate the pairwise distance calculation as solving an all-pairs shortest
path problem. To improve efficiency, we decompose this problem into a set of
single-source shortest path problems. The time complexity is reduced from
O(kn^2N) to O(knN). Architectures are hierarchically clustered according to the
distances between them. A circle-packing-based architecture visualization has
been developed to convey both the global relationships between clusters and
local neighborhoods of the architectures in each cluster. Two case studies and
a post-analysis are presented to demonstrate the effectiveness of ArchExplorer
in summarizing design principles and selecting better-performing architectures.
