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Presenting long sequences of dynamic graphs remains challenging due to theunderlying large-scale and high-dimensional data. We propose dg2pix, a novelpixel-based visualization technique, to visually explore temporal andstructural properties in long sequences of large-scale graphs. The approachconsists of three main steps: (1) the multiscale modeling of the temporaldimension; (2) unsupervised graph embeddings to learn low-dimensionalrepresentations of the dynamic graph data; and (3) an interactive pixel-basedvisualization to simultaneously explore the evolving data at different temporalaggregation scales. dg2pix provides a scalable overview of a dynamic graph,supports the exploration of long sequences of high-dimensional graph data, andenables the identification and comparison of similar temporal states. We showthe applicability of the technique to synthetic and real-world datasets,demonstrating that temporal patterns in dynamic graphs can be identified andinterpreted over time. dg2pix contributes a suitable intermediaterepresentation between node-link diagrams at the high detail end and matrixrepresentations on the low detail end.

dg2pix: Pixel-Based Visual Analysis of Dynamic Graphs