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dc.contributor.authorIñiguez, Gerardo
dc.contributor.authorHeydari, Sara
dc.contributor.authorKertész, János
dc.contributor.authorSaramäki, Jari
dc.date.accessioned2023-09-15T11:37:01Z
dc.date.available2023-09-15T11:37:01Z
dc.date.issued2023-08-26
dc.identifier.doi10.1038/s41467-023-40888-5
dc.identifier.urihttp://hdl.handle.net/20.500.14018/14127
dc.description.abstractTie strengths in social networks are heterogeneous, with strong and weak ties playing different roles at the network and individual levels. Egocentric networks, networks of relationships around an individual, exhibit few strong ties and more weaker ties, as evidenced by electronic communication records. Mobile phone data has also revealed persistent individual differences within this pattern. However, the generality and driving mechanisms of social tie strength heterogeneity remain unclear. Here, we study tie strengths in egocentric networks across multiple datasets of interactions between millions of people during months to years. We find universality in tie strength distributions and their individual-level variation across communication modes, even in channels not reflecting offline social relationships. Via a simple model of egocentric network evolution, we show that the observed universality arises from the competition between cumulative advantage and random choice, two tie reinforcement mechanisms whose balance determines the diversity of tie strengths. Our results provide insight into the driving mechanisms of tie strength heterogeneity in social networks and have implications for the understanding of social network structure and individual behavior.
dc.language.isoengen_US
dc.publisherSpringer Science and Business Media LLCen_US
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.titleUniversal patterns in egocentric communication networksen_US
dc.typeJournal article
dc.source.journaltitleNature Communications
dc.source.volume14
dc.source.issue1
dc.source.spage1
dc.source.epage9
dc.description.versionPublished version
refterms.dateFOA2023-09-16T01:58:46Z
dc.contributor.unitDepartment of Network and Data Science
dc.identifier.eissn2041-1723


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CC BY 4.0
Except where otherwise noted, this item's license is described as CC BY 4.0