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.
@article{arxiv.1211.7052,
title = {Quantifying the effect of temporal resolution on time-varying networks},
author = {Bruno Ribeiro and Nicola Perra and Andrea Baronchelli},
journal= {arXiv preprint arXiv:1211.7052},
year = {2013}
}