# DGSVis: Visual Analysis of Hierarchical Snapshots in Dynamic Graph

arXiv: https://arxiv.org/abs/2205.13220 · HTML: https://mlsanity.com/paper/2205.13220/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 58 references that are in the map.
- First posted on arXiv 26 May 2022.

## Builds on (most cited first, 9 of 9)

- [Visual Detection of Structural Changes in Time-Varying Graphs Using Persistent Homology](https://mlsanity.com/paper/1707.06683/) (2017)
- [Towards Better Bus Networks: A Visual Analytics Approach](https://mlsanity.com/paper/2008.10915/) (2020)
- [Towards a Survey on Static and Dynamic Hypergraph Visualizations](https://mlsanity.com/paper/2107.13936/) (2021)
- [PassVizor: Toward Better Understanding of the Dynamics of Soccer Passes](https://mlsanity.com/paper/2009.02464/) (2020)
- [Multiscale Snapshots: Visual Analysis of Temporal Summaries in Dynamic Graphs](https://mlsanity.com/paper/2008.08282/) (2020)
- [Simultaneous Matrix Orderings for Graph Collections](https://mlsanity.com/paper/2109.12050/) (2021)
- [Analyzing Time Attributes in Temporal Event Sequences](https://mlsanity.com/paper/1908.00903/) (2019)
- [The Effectiveness of Interactive Visualization Techniques for Time Navigation of Dynamic Graphs on Large Displays](https://mlsanity.com/paper/2008.12747/) (2020)
- [Staged Animation Strategies for Online Dynamic Networks](https://mlsanity.com/paper/2009.02005/) (2020)

## Abstract (arXiv)

Dynamic graph visualization attracts researchers' concentration as it represents time-varying relationships between entities in multiple domains (e.g., social media analysis, academic cooperation analysis, team sports analysis). Integrating visual analytic methods is consequential in presenting, comparing, and reviewing dynamic graphs. Even though dynamic graph visualization is developed for many years, how to effectively visualize large-scale and time-intensive dynamic graph data with subtle changes is still challenging for researchers. To provide an effective analysis method for this type of dynamic graph data, we propose a snapshot generation algorithm involving Human-In-Loop to help users divide the dynamic graphs into multi-granularity and hierarchical snapshots for further analysis. In addition, we design a visual analysis prototype system (DGSVis) to assist users in accessing the dynamic graph insights effectively. DGSVis integrates a graphical operation interface to help users generate snapshots visually and interactively. It is equipped with the overview and details for visualizing hierarchical snapshots of the dynamic graph data. To illustrate the usability and efficiency of our proposed methods for this type of dynamic graph data, we introduce two case studies based on basketball player networks in a competition. In addition, we conduct an evaluation and receive exciting feedback from experienced visualization experts.