Multiscale Snapshots: Visual Analysis of Temporal Summaries in Dynamic Graphs

TL;DR

This paper introduces Multiscale Snapshots, a visual analytics method for analyzing large-scale dynamic graphs across multiple temporal scales, enabling efficient exploration of temporal states, trends, and outliers.

Contribution

The paper presents a novel multiscale snapshot approach that combines recursive temporal summaries, graph embeddings, and multi-scale visualization for dynamic graph analysis.

Findings

Effective discovery of recurring temporal states

Accelerated analysis through low-dimensional embeddings

Successful application to real-world datasets

Abstract

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.