Scaling Properties in Time-Varying Networks with Memory

TL;DR

This paper investigates how activity and memory influence the structure and spreading dynamics in time-varying networks, proposing a coarsening method to analyze their effects on epidemic processes.

Contribution

It introduces a temporal-pattern coarsening approach to study the impact of activity and memory on epidemic spreading in dynamic networks.

Findings

Scaling relations between coarsening and spreading dynamics

Memory significantly affects epidemic thresholds

Universal behaviors observed across different memory-causality tests

Abstract

The formation of network structure is mainly influenced by an individual node's activity and its memory, where activity can usually be interpreted as the individual inherent property and memory can be represented by the interaction strength between nodes. In our study, we define the activity through the appearance pattern in the time-aggregated network representation, and quantify the memory through the contact pattern of empirical temporal networks. To address the role of activity and memory in epidemics on time-varying networks, we propose temporal-pattern coarsening of activity-driven growing networks with memory. In particular, we focus on the relation between time-scale coarsening and spreading dynamics in the context of dynamic scaling and finite-size scaling. Finally, we discuss the universality issue of spreading dynamics on time-varying networks for various memory-causality tests.