biostudies-literatureUnknown3Ribeiro BTime-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 ?t. In this work we quantify the impact of an arbitrary ?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.Scientific reports3006https://www.ebi.ac.uk/biostudies/studies/S-EPMC3801130biostudies-literatureQuantifying the effect of temporal resolution on time-varying networks.PMC3801130Baronchelli APerra NRibeiro BfalseQuantifying the effect of temporal resolution on time-varying networks.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 ?t. In this work we quantify the impact of an arbitrary ?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.2013-01-01T00:00:00Z20132021-02-20T12:10:16Z2019-03-27T01:17:24ZS-EPMC38011302414169510.1038/srep03006