Perception of own centrality in social networks

This study explores how individuals perceive their social networks, with a focus on their own positioning. Using experimental methods and network analysis, we show that people have a limited understanding of their social standing in terms of popularity (in-degree) and centrality. Few participants accurately estimate their popularity, and even fewer correctly identify their decile of centrality. A similar pattern emerges for their perceptions of the most popular and central individuals, but we find no correlation between the ability to assess one's own position and the ability to detect key network members. Popular participants correctly perceive themselves as more popular, although they tend to misjudge their popularity more than less popular peers. They are nonetheless more accurate in estimating their centrality. Perceived centrality is only weakly correlated with actual centrality, but central individuals misperceive both their popularity and centrality to a greater extent. We further show that these misperceptions have real-world implications. Conditional on network positioning, students who see themselves as less popular and less central-and those with more accurate self-perceptions-tend to achieve higher grades, whereas individuals recognized by others as popular and central perform significantly better academically. These findings challenge theoretical models that assume accurate self-awareness of network positions and highlight the need to reconsider the implications for key-player interventions in public health, education, and organizational contexts.

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Publication Details

Journal
Proceedings of the National Academy of Sciences
Published
2025-11-21
DOI
https://doi.org/10.1073/pnas.2420334122
Primary Topic
Complex Network Analysis Techniques
Type
article
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article

Perception of own centrality in social networks

Pablo Brañas‐Garza, Jaromír Kovářík, Juan Ozaita, Ángel Sánchez
Proceedings of the National Academy of Sciences
Complex Network Analysis Techniques
article

Perception of own centrality in social networks

Pablo Brañas‐Garza, Jaromír Kovářík, Juan Ozaita, Ángel Sánchez
article en

Abstract

This study explores how individuals perceive their social networks, with a focus on their own positioning. Using experimental methods and network analysis, we show that people have a limited understanding of their social standing in terms of popularity (in-degree) and centrality. Few participants accurately estimate their popularity, and even fewer correctly identify their decile of centrality. A similar pattern emerges for their perceptions of the most popular and central individuals, but we find no correlation between the ability to assess one's own position and the ability to detect key network members. Popular participants correctly perceive themselves as more popular, although they tend to misjudge their popularity more than less popular peers. They are nonetheless more accurate in estimating their centrality. Perceived centrality is only weakly correlated with actual centrality, but central individuals misperceive both their popularity and centrality to a greater extent. We further show that these misperceptions have real-world implications. Conditional on network positioning, students who see themselves as less popular and less central-and those with more accurate self-perceptions-tend to achieve higher grades, whereas individuals recognized by others as popular and central perform significantly better academically. These findings challenge theoretical models that assume accurate self-awareness of network positions and highlight the need to reconsider the implications for key-player interventions in public health, education, and organizational contexts.

Proceedings of the National Academy of SciencesVol. 122(47)
University of the Basque Country (ES), Universidad de Zaragoza (ES), Institute for Cross-Disciplinary Physics and Complex Systems (ES), Universidad Loyola (BO), Biocom (US), Grupo ACS (Spain) (ES), Instituto de Biocomputación y Física de Sistemas Complejos, Universidad Carlos III de Madrid (ES), University of West Bohemia in Pilsen (CZ)
Openalex Percentile: Top 71%
Complex Network Analysis Techniques
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