Quantifying the effect of temporal resolution on time-varying networks

TL;DR

This paper investigates how the choice of temporal resolution affects the analysis of time-varying networks, providing a mathematical framework to understand biases introduced by data aggregation.

Contribution

It introduces a simple mathematical model to quantify the impact of temporal resolution on dynamical processes in time-varying networks, especially for random walks.

Findings

Analytical description of bias due to time aggregation

Framework applicable to real datasets

Improves understanding of dynamical processes on temporal networks

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 \Delta t. In this work we quantify the impact of an arbitrary \Delta 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.