  We propose dynamic scaling in temporal networks with heterogeneous activities
and memory, and provide a comprehensive picture for the dynamic topologies of
such networks, in terms of the modified activity-driven network model [H. Kim
\textit{et al.}, Eur. Phys. J. B {\bf 88}, 315 (2015)]. Particularly, we focus
on the interplay of the time resolution and memory in dynamic topologies.
Through the random walk (RW) process, we investigate diffusion properties and
topological changes as the time resolution increases. Our results with memory
are compared to those of the memoryless case. Based on the temporal percolation
concept, we derive scaling exponents in the dynamics of the largest cluster and
the coverage of the RW process in time-varying networks. We find that the time
resolution in the time-accumulated network determines the effective size of the
network, while memory affects relevant scaling properties at the crossover from
the dynamic regime to the static one. The origin of memory-dependent scaling
behaviors is the dynamics of the largest cluster, which depends on temporal
degree distributions. Finally, we conjecture of the extended finite-size
scaling ansatz for dynamic topologies and the fundamental property of temporal
networks, which are numerically confirmed.
