dg2pix

Presenting long sequences of dynamic graphs remains challenging due to the underlying large-scale and high-dimensional data. Dynamic graph to pixel-based visualization, dg2pix, is a novel pixel-based visualization technique, used 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.