dg2pix: Pixel-Based Visual Analysis of Dynamic Graphs

Eren Cakmak, Dominik Jäckle, Tobias Schreck, Daniel Keim

External link (DOI)
View presentation:2020-10-26T16:45:00ZGMT-0600Change your timezone on the schedule page
2020-10-26T16:45:00Z
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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. The approach consists of three main adjustable steps (1) temporal aggregations, (2) graph embeddings, and (3) the visual mapping to the pixel-based visualization. The main idea is to visually analyze the latent space to identify temporal changes in the dynamic graph. 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.
Keywords

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 explore the evolving data at different temporal aggregation scales simultaneously. 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 easily identified and interpreted over time. Our dg2pix contributes a suitable intermediate representation between node-link diagrams at the high detail end, and matrix representations on the low detail end.