dg2pix: Pixel-Based Visual Analysis of Dynamic Graphs

TL;DR

dg2pix is a novel pixel-based visualization method that enables scalable exploration and analysis of long sequences of large-scale dynamic graphs by combining multiscale temporal modeling, unsupervised embeddings, and interactive visualization.

Contribution

It introduces dg2pix, a new visualization technique that effectively visualizes large-scale dynamic graphs over time, bridging the gap between detailed node-link diagrams and matrix representations.

Findings

Successfully visualizes synthetic and real-world dynamic graphs.

Identifies and interprets temporal patterns in large-scale graph data.

Supports exploration of long graph sequences at multiple temporal scales.

Abstract

Presenting long sequences of dynamic graphs remains challenging due to the underlying large-scale and high-dimensional data. We propose dg2pix, a novel pixel-based visualization technique, to visually explore temporal and structural properties in long sequences of large-scale graphs. The approach consists of three main steps: (1) the multiscale modeling of the temporal dimension; (2) unsupervised graph embeddings to learn low-dimensional representations of the dynamic graph data; and (3) an interactive pixel-based visualization to simultaneously explore the evolving data at different temporal aggregation scales. dg2pix provides a scalable overview of a dynamic graph, supports the exploration of long sequences of high-dimensional graph data, and enables the identification and comparison of similar temporal states. We show the applicability of the technique to synthetic and real-world datasets, demonstrating that temporal patterns in dynamic graphs can be identified and interpreted over time. dg2pix contributes a suitable intermediate representation between node-link diagrams at the high detail end and matrix representations on the low detail end.