Universal patterns in egocentric communication networks

Gerardo Iñiguez (), Sara Heydari, János Kertész and Jari Saramäki ()
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Gerardo Iñiguez: Central European University
Sara Heydari: Aalto University School of Science
János Kertész: Central European University
Jari Saramäki: Aalto University School of Science

Nature Communications, 2023, vol. 14, issue 1, 1-9

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-40888-5

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