Why friends in common reveal network stars

The Friendship Paradox states that, on average, your friends have more friends than you do. We extend this to common friends-those who appear in multiple people's friend lists. We show that the more people who share a common friend, the more connected that person tends to be, and we derive an expression quantifying this progression. In a regional Facebook network, a common friend to three randomly sampled individuals has on average more friends than 99.9% of the network. In a citation network, a source cited by any two of a random sample of papers has on average more citations than 99.99% of cited works. This power of common friends is most pronounced when few nodes hold disproportionate shares of ties, typical of networks involving superspreaders of disease, mega-influencers online, and highly connected nodes in neural networks. We discuss implications for network sampling, targeted interventions, social perception, and network dynamics.

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Publication Details

Journal
Proceedings of the National Academy of Sciences
Published
2026-08-26
DOI
https://doi.org/10.1073/pnas.2603299123
Primary Topic
Complex Network Analysis Techniques
Type
article
Field-Weighted Citation Impact
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article

Why friends in common reveal network stars

Scott L. Feld, Alec McGail
Proceedings of the National Academy of Sciences
Complex Network Analysis Techniques
article

Why friends in common reveal network stars

Scott L. Feld, Alec McGail
article en

Abstract

The Friendship Paradox states that, on average, your friends have more friends than you do. We extend this to common friends-those who appear in multiple people's friend lists. We show that the more people who share a common friend, the more connected that person tends to be, and we derive an expression quantifying this progression. In a regional Facebook network, a common friend to three randomly sampled individuals has on average more friends than 99.9% of the network. In a citation network, a source cited by any two of a random sample of papers has on average more citations than 99.99% of cited works. This power of common friends is most pronounced when few nodes hold disproportionate shares of ties, typical of networks involving superspreaders of disease, mega-influencers online, and highly connected nodes in neural networks. We discuss implications for network sampling, targeted interventions, social perception, and network dynamics.

Proceedings of the National Academy of SciencesVol. 123(35)
Purdue University West Lafayette (US)
Reduced inequalities
Openalex Percentile: Top 10%
Complex Network Analysis Techniques
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