Quantifying the effect of temporal resolution on time-varying networks

TLDR

Time‑varying networks model systems whose nodes and interactions change over time, yet are often represented as sequences of static networks aggregated over a time interval Δt. In this work we quantify the impact of an arbitrary Δt on the description of a dynamical process taking place upon a time‑varying network. We focus on the elementary random walk and put forth a simple mathematical framework that well describes the behavior observed on real datasets. The analytical description of the bias introduced by time‑integrating techniques represents a step forward in the correct characterization of dynamical processes on time‑varying graphs.

Abstract

Abstract Time-varying networks describe a wide array of systems whose constituents and interactions evolve over time. They are defined by an ordered stream of interactions between nodes, yet they are often represented in terms of a sequence of static networks, each aggregating all edges and nodes present in a time interval of size Δ t . In this work we quantify the impact of an arbitrary Δ t on the description of a dynamical process taking place upon a time-varying network. We focus on the elementary random walk and put forth a simple mathematical framework that well describes the behavior observed on real datasets. The analytical description of the bias introduced by time integrating techniques represents a step forward in the correct characterization of dynamical processes on time-varying graphs.