  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.
