Multiscale Snapshots: Visual Analysis of Temporal Summaries in Dynamic Graphs
This addresses the problem of analyzing temporal patterns in dynamic graphs for researchers and analysts, though it appears incremental as it builds on existing visual analytics and embedding techniques.
The authors tackled the challenge of visually analyzing large-scale dynamic graphs by proposing Multiscale Snapshots, a visual analytics approach that generates temporal summaries at multiple scales, uses graph embeddings for efficient analysis, and enables discovery of similar states and trends, with demonstrated usefulness through quantitative evaluation and real-world application.
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.