  Collective cooperation drives the dynamics of many natural, social, and
economic phenomena, making understanding the evolution of cooperation with
evolutionary game theory a central question of modern science. Although human
interactions are best described as complex networks, current explorations are
limited to static networks where interactions represented by network links are
permanent and do not change over time. In reality, human activities often
involve temporal interactions, where links are impermanent, and understanding
the evolution of cooperation on such ubiquitous temporal networks is an open
question. Here, we present a general framework for systematically analyzing how
collective cooperation evolves on any temporal network, which unifies the study
of evolutionary game dynamics with dynamic and static interactions. We show
that the emergence of cooperation is facilitated by a simple rule of thumb:
hubs (individuals with many social ties) should be temporally deprioritized in
interactions. We further provide a quantitative metric capturing the priority
of hubs, which we utilize to orchestrate the ordering of interactions to best
promote cooperation on empirical temporal networks. Our findings unveil the
fundamental advantages conferred by temporal interactions for promoting
collective cooperation, which transcends the specific insights gleaned from
studying traditional static snapshots.
