Tendencies toward triadic closure: Field experimental evidence

Empirical social networks are characterized by a high degree of triadic closure (i.e., transitivity, clustering): network neighbors of the same individual are also likely to be directly connected. It is unknown to what degree this results from dispositions to form such ties (i.e., to close open triangles) per se versus other processes such as homophily and more opportunities for exposure. These mechanisms are difficult to disentangle in many settings. On social media, however, they can be decomposed - and platforms frequently make decisions that depend on these distinct processes. Here, using a field experiment on social media, we randomize the existing network structure that a user faces when they are followed by a target account that we control. We then examine whether the user reciprocates this tie formation. Being randomly assigned to have an existing tie to an account that follows the target user increases tie formation by 35%. Through multiple control conditions, we attribute this effect specifically to a minimal cue that indicates the presence of a potential mutual follower. Theory suggests that triadic closure should be especially likely in open triads of strong ties, and accordingly we find larger effects when the subject has interacted more with the existing follower. These results indicate a substantial role for tendencies toward triadic closure, but one that is substantially smaller than what might be inferred from prior observational studies. Platforms and others may rely on these tendencies in encouraging tie formation, with broader implications for network structure and information diffusion in online networks.

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Publication Details

Journal
Proceedings of the National Academy of Sciences
Published
2025-06-30
DOI
https://doi.org/10.1073/pnas.2404590122
Citations
2
Primary Topic
Complex Network Analysis Techniques
Type
article
Field-Weighted Citation Impact
2.65
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article

Tendencies toward triadic closure: Field experimental evidence

Mohsen Mosleh, David G. Rand, Dean Eckles
2 citations
Proceedings of the National Academy of Sciences
Complex Network Analysis Techniques
2.65
article

Tendencies toward triadic closure: Field experimental evidence

Mohsen Mosleh, David G. Rand, Dean Eckles
article en
2 citations

Abstract

Empirical social networks are characterized by a high degree of triadic closure (i.e., transitivity, clustering): network neighbors of the same individual are also likely to be directly connected. It is unknown to what degree this results from dispositions to form such ties (i.e., to close open triangles) per se versus other processes such as homophily and more opportunities for exposure. These mechanisms are difficult to disentangle in many settings. On social media, however, they can be decomposed - and platforms frequently make decisions that depend on these distinct processes. Here, using a field experiment on social media, we randomize the existing network structure that a user faces when they are followed by a target account that we control. We then examine whether the user reciprocates this tie formation. Being randomly assigned to have an existing tie to an account that follows the target user increases tie formation by 35%. Through multiple control conditions, we attribute this effect specifically to a minimal cue that indicates the presence of a potential mutual follower. Theory suggests that triadic closure should be especially likely in open triads of strong ties, and accordingly we find larger effects when the subject has interacted more with the existing follower. These results indicate a substantial role for tendencies toward triadic closure, but one that is substantially smaller than what might be inferred from prior observational studies. Platforms and others may rely on these tendencies in encouraging tie formation, with broader implications for network structure and information diffusion in online networks.

Proceedings of the National Academy of SciencesVol. 122(27)
Internet Society (US), University of Oxford (GB), Institute of Cognitive and Brain Sciences (US), Data & Society Research Institute (US), Massachusetts Institute of Technology (US)
Reduced inequalities
Openalex Percentile: Top 10%
Complex Network Analysis Techniques
2.65
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