2016/12/24 by Xin-Zeng Wu, Wu, Xin-Zeng, Allon G. Percus +3 · 3 citations
Physics and Astronomy · Psychology · #Complex Network Analysis Techniques #Computers and Society (cs.CY) #FOS: Computer and information sciences #FOS: Physical sciences #Mental Health Research Topics #Opinion Dynamics and Social Influence #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI) #Statistical Mechanics (cond-mat.stat-mech)
paper · pdf · doi:10.48550/arxiv.1612.08200
openalex publication_date 2016/12/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In numerous physical models on networks, dynamics are based on interactions that exclusively involve properties of a node's nearest neighbors. However, a node's local view of its neighbors may systematically bias perceptions of network connectivity or the prevalence of certain traits. We investigate the strong friendship paradox, which occurs when the majority of a node's neighbors have more neighbors than does the node itself. We develop a model to predict the magnitude of the paradox, showing that it is enhanced by negative correlations between degrees of neighboring nodes. We then show that by including neighbor-neighbor correlations, which are degree correlations one step beyond those of neighboring nodes, we accurately predict the impact of the strong friendship paradox in real-world networks. Understanding how the paradox biases local observations can inform better measurements of network structure and our understanding of collective phenomena.