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, 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 us to discover similar temporal summaries (e.g., reoccurring 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.

Automated summary

The proposed Multiscale Snapshots approach tackles the challenge of analyzing large-scale dynamic graphs by providing a multi-scale visual analysis of temporal summaries. It recursively generates temporal summaries, utilizes graph embeddings, and visualizes data evolving from coarse to fine-granular snapshots to semi-automatically analyze temporal states, trends, and outliers. This allows for the discovery of similar temporal summaries, reduces temporal data for faster automatic analysis, and explores both structural and temporal properties of dynamic graphs.