  How do social networks differ across platforms? How do information networks
change over time? Answering questions like these requires us to compare two or
more graphs. This task is commonly treated as a measurement problem, but
numerical answers give limited insight. Here, we argue that if the goal is to
gain understanding, we should treat graph similarity assessment as a
description problem instead. We formalize this problem as a model selection
task using the Minimum Description Length principle, capturing the similarity
of the input graphs in a common model and the differences between them in
transformations to individual models. To discover good models, we propose Momo,
which breaks the problem into two parts and introduces efficient algorithms for
each. Through an extensive set of experiments on a wide range of synthetic and
real-world graphs, we confirm that Momo works well in practice.
