Multiscale Snapshots: Visual Analysis of Temporal Summaries in Dynamic Graphs

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