{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["3"],"submitter":["Ribeiro B"],"pubmed_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 ?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."],"journal":["Scientific reports"],"pagination":["3006"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC3801130"],"repository":["biostudies-literature"],"pubmed_title":["Quantifying the effect of temporal resolution on time-varying networks."],"pmcid":["PMC3801130"],"pubmed_authors":["Baronchelli A","Perra N","Ribeiro B"],"additional_accession":[]},"is_claimable":false,"name":"Quantifying the effect of temporal resolution on time-varying networks.","description":"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.","dates":{"release":"2013-01-01T00:00:00Z","publication":"2013","modification":"2021-02-20T12:10:16Z","creation":"2019-03-27T01:17:24Z"},"accession":"S-EPMC3801130","cross_references":{"pubmed":["24141695"],"doi":["10.1038/srep03006"]}}