dg2pix: Pixel-Based Visual Analysis of Dynamic Graphs
This addresses visualization difficulties for researchers analyzing large-scale, high-dimensional dynamic graph data, though it appears incremental as a hybrid method building on existing representations.
The paper tackles the challenge of visualizing long sequences of dynamic graphs by proposing dg2pix, a pixel-based technique that enables scalable overview and identification of temporal patterns, as demonstrated on synthetic and real-world datasets.
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.