Universal patterns in egocentric communication networks

TL;DR

This study uncovers a universal pattern in the distribution of tie strengths within egocentric social networks across various communication modes, driven by competition between cumulative advantage and random choice.

Contribution

It introduces an analytically tractable model explaining the universality of tie strength heterogeneity based on fundamental reinforcement mechanisms.

Findings

Universal distribution of tie strengths across communication modes

Heterogeneity driven by competition between reinforcement mechanisms

Model explains individual differences in egocentric networks

Abstract

Tie strengths in social networks are heterogeneous, with strong and weak ties playing different roles at both the network and the individual level. Egocentric networks, networks of relationships around a focal individual, exhibit a small number of strong ties and a larger number of weaker ties, a pattern that is evident in electronic communication records, such as mobile phone calls. Mobile phone data has also revealed persistent individual differences within this pattern. However, the generality and the driving mechanisms of this tie strength heterogeneity remain unclear. Here, we study tie strengths in egocentric networks across multiple datasets containing records of interactions between millions of people over time periods ranging from months to years. Our findings reveal a remarkable universality in the distribution of tie strengths and their individual-level variation across different modes of communication, even in channels that may not reflect offline social relationships. With the help of an analytically tractable model of egocentric network evolution, we show that the observed universality can be attributed to the competition between cumulative advantage and random choice, two general mechanisms of tie reinforcement whose balance determines the amount of heterogeneity in tie strengths. Our results provide new insights into the driving mechanisms of tie strength heterogeneity in social networks and have implications for the understanding of social network structure and individual behavior.