  Human social behavior plays a crucial role in how pathogens like SARS-CoV-2
or fake news spread in a population. Social interactions determine the contact
network among individuals, while spreading, requiring individual-to-individual
transmission, takes place on top of the network. Studying the topological
aspects of a contact network, therefore, not only has the potential of leading
to valuable insights into how the behavior of individuals impacts spreading
phenomena, but it may also open up possibilities for devising effective
behavioral interventions. Because of the temporal nature of interactions -
since the topology of the network, containing who is in contact with whom,
when, for how long, and in which precise sequence, varies (rapidly) in time -
analyzing them requires developing network methods and metrics that respect
temporal variability, in contrast to those developed for static (i.e.,
time-invariant) networks. Here, by means of event mapping, we propose a method
to quantify how quickly agents mingle by transforming temporal network data of
agent contacts. We define a novel measure called 'contact sequence centrality',
which quantifies the impact of an individual on the contact sequences,
reflecting the individual's behavioral potential for spreading. Comparing
contact sequence centrality across agents allows for ranking the impact of
agents and identifying potential 'behavioral super-spreaders'. The method is
applied to social interaction data collected at an art fair in Amsterdam. We
relate the measure to the existing network metrics, both temporal and static,
and find that (mostly at longer time scales) traditional metrics lose their
resemblance to contact sequence centrality. Our work highlights the importance
of accounting for the sequential nature of contacts when analyzing social
interactions.
