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.

The social media website last.fm provides a detailed snapshot of what its users in hundreds of cities listen to each week. After suitably normalizing this data, we use it to test one hypothesis related to the geographic flow of music: that some cities are consistently early adopters of new music (and early to snub stale music). To test this hypothesis, we adapt a method previously used to detect the leadership networks present in flocks of birds. We find empirical support for the claim that a similar leadership network exists among cities.