Empirical social networks are often aggregate proxies for several heterogeneous relations. In online social networks, for instance, interactions related to friendship, kinship, business, interests, and other relationships may all be represented as catchall "friendships." Because several relations are mingled into one, the resulting networks exhibit relatively high and uniform density. As a consequence, the variation in positional differences and local cohesion may be too small for reliable analysis.

State-of-the-art link prediction utilizes combinations of complex features derived from network panel data. We here show that computationally less expensive features can achieve the same performance in the common scenario in which the data is available as a sequence of interactions. Our features are based on social vector clocks, an adaptation of the vector-clock concept introduced in distributed computing to social interaction networks. In fact, our experiments suggest that by taking into account the order and spacing of interactions, social vector clocks exploit different aspects of link formation so that their combination with previous approaches yields the most accurate predictor to date.

Abstract We propose a general technique to visualize multivariate data sequences. It is based on a symbiotic combination of three powerful concepts from information visualization: sparklines, glyphs and gestalt theory. By visualizing several well‐known data sets in new ways we first demonstrate how explicit consideration of gestalt principles can be used to leverage visual perception capabilities for the identification of patterns such as trends, periodicities, change points, or outliers. A more detailed case study with complex and noisy data from a psychological experiment then demonstrates how basic design ideas for gestaltlines can be applied in less controlled, and thus more realistic, situations. The case study is complemented with reports on feedback from domain experts and a user study, both indicating that gestaltlines can be a convenient and valid means to explore and communicate patterns in micro‐visualizations.

In modeling and analysis of longitudinal social networks, visual exploration is used in particular to complement and inform other methods. The most common graphical representations for this purpose appear to be animations and small multiples of intermediate states, depending on the type of media available. We present an alternative approach based on matrix representation of gestaltlines (a combination of Tufte's sparklines with glyphs based on gestalt theory). As a result, we obtain static, compact, yet data-rich diagrams that support specifically the exploration of evolving dyadic relations and persistent group structure, although at the expense of cross-sectional network views and indirect linkages.