  The overview-driven visual analysis of large-scale dynamic graphs poses a
major challenge. We propose Multiscale Snapshots, a visual analytics approach
to analyze temporal summaries of dynamic graphs at multiple temporal scales.
First, we recursively generate temporal summaries to abstract overlapping
sequences of graphs into compact snapshots. Second, we apply graph embeddings
to the snapshots to learn low-dimensional representations of each sequence of
graphs to speed up specific analytical tasks (e.g., similarity search). Third,
we visualize the evolving data from a coarse to fine-granular snapshots to
semi-automatically analyze temporal states, trends, and outliers. The approach
enables to discover similar temporal summaries (e.g., recurring states),
reduces the temporal data to speed up automatic analysis, and to explore both
structural and temporal properties of a dynamic graph. We demonstrate the
usefulness of our approach by a quantitative evaluation and the application to
a real-world dataset.
