# Multiscale Snapshots: Visual Analysis of Temporal Summaries in Dynamic Graphs

arXiv: https://arxiv.org/abs/2008.08282 · HTML: https://mlsanity.com/paper/2008.08282/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2020.

## Built on by (most cited first, 3 of 3)

- [Interactive Narrative Analytics: Bridging Computational Narrative Extraction and Human Sensemaking](https://mlsanity.com/paper/2601.11459/) (2026)
- [DGSVis: Visual Analysis of Hierarchical Snapshots in Dynamic Graph](https://mlsanity.com/paper/2205.13220/) (2022)
- [Visualizing the Passage of Time with Video Temporal Pyramids](https://mlsanity.com/paper/2208.11885/) (2022)

## Abstract (arXiv)

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.