  Activity-driven modeling has been recently proposed as an alternative growth
mechanism for time varying networks, displaying power-law degree distribution
in time-aggregated representation. This approach assumes memoryless agents
developing random connections, thus leading to random networks that fail to
reproduce two-nodes degree correlations and the high clustering coefficient
widely observed in real social networks. In this work we introduce these
missing topological features by accounting for memory effects on the dynamic
evolution of time-aggregated networks. To this end, we propose an
activity-driven network growth model including a triadic-closure step as main
connectivity mechanism. We show that this mechanism provides some of the
fundamental topological features expected for social networks. We derive
analytical results and perform extensive numerical simulations in regimes with
and without population growth. Finally, we present two cases of study, one
comprising face-to-face encounters in a closed gathering, while the other one
from an online social friendship network.

Physics and Society;Social and Information Networks