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Activity-driven modeling has been recently proposed as an alternative growthmechanism for time varying networks, displaying power-law degree distributionin time-aggregated representation. This approach assumes memoryless agentsdeveloping random connections, thus leading to random networks that fail toreproduce two-nodes degree correlations and the high clustering coefficientwidely observed in real social networks. In this work we introduce thesemissing topological features by accounting for memory effects on the dynamicevolution of time-aggregated networks. To this end, we propose anactivity-driven network growth model including a triadic-closure step as mainconnectivity mechanism. We show that this mechanism provides some of thefundamental topological features expected for social networks. We deriveanalytical results and perform extensive numerical simulations in regimes withand without population growth. Finally, we present two cases of study, onecomprising face-to-face encounters in a closed gathering, while the other onefrom an online social friendship network.

Memory effects induce structure in social networks with activity-driven agents