{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Bauza Mingueza F"],"funding":["Generalitat de Catalunya","Universitat Rovira i Virgili","European Research Council","Ministerio de Ciencia e Innovación","Ministerio de Economía y Competitividad","James S. McDonnell Foundation","Gobierno de Aragón"],"pagination":["765"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9840642"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["13(1)"],"pubmed_abstract":["Many complex networked systems exhibit volatile dynamic interactions among their vertices, whose order and persistence reverberate on the outcome of dynamical processes taking place on them. To quantify and characterize the similarity of the snapshots of a time-varying network-a proxy for the persistence,-we present a study on the persistence of the interactions based on a descriptor named temporality. We use the average value of the temporality, [Formula: see text], to assess how \"special\" is a given time-varying network within the configuration space of ordered sequences of snapshots. We analyse the temporality of several empirical networks and find that empirical sequences are much more similar than their randomized counterparts. We study also the effects on [Formula: see text] induced by the (time) resolution at which interactions take place."],"journal":["Scientific reports"],"pubmed_title":["Characterization of interactions' persistence in time-varying networks."],"pmcid":["PMC9840642"],"funding_grant_id":["2017SGR-896","PGC2018-094754-B-C2","2019PFR-URV-B2-41","E36_20R","#220020325","E30_17R","PID2020-113582GB-I00","803860","IJCI-2017-34300","PGC2018-094684-B-C22"],"pubmed_authors":["Arenas A","Bauza Mingueza F","Floria M","Cardillo A","Gomez-Gardenes J"],"additional_accession":[]},"is_claimable":false,"name":"Characterization of interactions' persistence in time-varying networks.","description":"Many complex networked systems exhibit volatile dynamic interactions among their vertices, whose order and persistence reverberate on the outcome of dynamical processes taking place on them. To quantify and characterize the similarity of the snapshots of a time-varying network-a proxy for the persistence,-we present a study on the persistence of the interactions based on a descriptor named temporality. We use the average value of the temporality, [Formula: see text], to assess how \"special\" is a given time-varying network within the configuration space of ordered sequences of snapshots. We analyse the temporality of several empirical networks and find that empirical sequences are much more similar than their randomized counterparts. We study also the effects on [Formula: see text] induced by the (time) resolution at which interactions take place.","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Jan","modification":"2025-04-04T08:25:21.131Z","creation":"2025-04-04T08:25:21.131Z"},"accession":"S-EPMC9840642","cross_references":{"pubmed":["36641475"],"doi":["10.1038/s41598-022-25907-7"]}}