  Many data analysis problems rely on dynamic networks, such as social or
communication network analyses. Providing a scalable overview of long sequences
of such dynamic networks remains challenging due to the underlying large-scale
data containing elusive topological changes. We propose two complementary
pixel-based visualizations, which reflect occurrences of selected sub-networks
(motifs) and provide a time-scalable overview of dynamic networks: a
network-level census (motif significance profiles) linked with a node-level
sub-network metric (graphlet degree vectors) views to reveal structural
changes, trends, states, and outliers. The network census captures
significantly occurring motifs compared to their expected occurrences in random
networks and exposes structural changes in a dynamic network. The sub-network
metrics display the local topological neighborhood of a node in a single
network belonging to the dynamic network. The linked pixel-based visualizations
allow exploring motifs in different-sized networks to analyze the changing
structures within and across dynamic networks, for instance, to visually
analyze the shape and rate of changes in the network topology. We describe the
identification of visual patterns, also considering different reordering
strategies to emphasize visual patterns. We demonstrate the approach's
usefulness by a use case analysis based on real-world large-scale dynamic
networks, such as the evolving social networks of Reddit or Facebook.
