Do the Young Live in a "Smaller World" Than the Old? Age-Specific Degrees of Separation in Human Communication
Not on the map. Why?
Abstract (arXiv)
In this paper, we investigate the phenomenon of "age-specific small worlds" using data from a large-scale mobile communication network approximating interaction patterns at societal scale. Rather than asking whether two random individuals are separated by a small number of links, we ask whether individuals in specific age groups live in a small world in relation to individuals from other age groups. Our analysis shows that there is systematic variation in this age-relative small world effect. Young people live in the "smallest world," being separated from other young people and their parent's generation via a smaller number of intermediaries than older individuals. The oldest people live in the "least small world," being separated from their same age peers and their younger counterparts by a larger number of intermediaries. Variation in the small world effect is specific to age as a node attribute (being absent in the case of gender) and is consistently observed under several data robustness checks. The discovery of age-specific small worlds is consistent with well-known social mechanisms affecting the way age interacts with network connectivity and the relative prevalence of kin ties and non-kin ties observed in this network. This social pattern has significant implications for our understanding of generation-specific dynamics of information cascades, diffusion phenomena, and the spread of fads and fashions.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Jun 2016.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2022: 3; 2023: 0; 2024: 1.
4 papers.
- 2022A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainabilitycited by 118
- 2022FMP: Toward Fair Graph Message Passing against Topology Biascited by 35
- 2024Fair Bilevel Neural Network (FairBiNN): On Balancing fairness and accuracy via Stackelberg Equilibriumcited by 9
- 2022Distraction is All You Need for Fairnesscited by 2
- 2024Fair Bilevel Neural Network (FairBiNN): On Balancing fairness and accuracy via Stackelberg Equilibrium21 Oct 2024
- 2022A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability18 Apr 2022
- 2022Distraction is All You Need for Fairness15 Mar 2022
- 2022FMP: Toward Fair Graph Message Passing against Topology Bias8 Feb 2022