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Universal patterns in egocentric communication networks

  • Central European University
  • Complexity Science Hub Vienna
  • Universidad Nacional Autónoma de México
  • Central European University Vienna
  • Tampere University

Research output: Contribution to journalArticleScientificpeer-review

15 Citations (Scopus)
122 Downloads (Pure)

Abstract

Tie 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.

Original languageEnglish
Article number5217
Pages (from-to)1-9
Number of pages9
JournalNature Communications
Volume14
Issue number1
DOIs
Publication statusPublished - Dec 2023
MoE publication typeA1 Journal article-refereed

Funding

G.I. thanks Tiina Näsi for valuable suggestions. G.I. and J.K. acknowledge support from AFOSR (Grant No. FA8655-20-1-7020), project EU H2020 Humane AI-net (Grant No. 952026), and CHIST-ERA project SAI (Grant No. FWF I 5205-N). J.K. acknowledges support from European Union’s Horizon 2020 research and innovation programme under grant agreement ERC No 810115 - DYNASNET. We acknowledge the computational resources provided by the Aalto Science–IT project. The study was part of the NetResilience consortium funded by the Strategic Research Council at the Academy of Finland (grant numbers 345188 and 345183). G.I. thanks Tiina Näsi for valuable suggestions. G.I. and J.K. acknowledge support from AFOSR (Grant No. FA8655-20-1-7020), project EU H2020 Humane AI-net (Grant No. 952026), and CHIST-ERA project SAI (Grant No. FWF I 5205-N). J.K. acknowledges support from European Union’s Horizon 2020 research and innovation programme under grant agreement ERC No 810115 - DYNASNET. We acknowledge the computational resources provided by the Aalto Science–IT project. The study was part of the NetResilience consortium funded by the Strategic Research Council at the Academy of Finland (grant numbers 345188 and 345183).

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