Dynamic topologies of activity-driven temporal networks with memory

摘要: We propose dynamic scaling in temporal networks with heterogeneous activities and memory provide a comprehensive picture for the topologies of such networks, terms modified activity-driven network model [H. Kim et al., Eur. Phys. J. B 88, 315 (2015)EPJBFY1434-602810.1140/epjb/e2015-60662-7]. Particularly, we focus on interplay time resolution topologies. Through random-walk (RW) process, investigate diffusion properties topological changes as increases. Our results are compared to those memoryless case. Based percolation concept, derive exponents dynamics largest cluster coverage RW process time-varying networks. find that time-accumulated determines effective size network, while affects relevant at crossover from regime static one. The origin memory-dependent behaviors is cluster, which depends degree distributions. Finally, conjecture extended finite-size ansatz fundamental property numerically confirmed.