The Effect of Recommendations on Network Structure
Abstract
Online social networks regularly offer users personalized, algorithmic suggestions of whom to connect to. Here we examine the aggregate effects of such recommendations on network structure, focusing on whether these recommendations increase the popularity of niche users or, conversely, those who are already popular. We investigate this issue by empirically and theoretically analyzing abrupt changes in Twitter's network structure around the mid-2010 introduction of its "Who to Follow" feature. We find that users across the popularity spectrum benefitted from the recommendations; however, the most popular users profited substantially more than average. We trace this "rich get richer" phenomenon to three intertwined factors. First, as is typical of network recommenders, the system relies on a "friend-of-friend"-style algorithm, which we show generally results in users being recommended proportional to their degree. Second, we find that the baseline growth rate of users is sublinear in degree. This mismatch between the recommender and the natural network dynamics thus alters the structural evolution of the network. Finally, we find that people are much more likely to respond positively to recommendations for popular users---perhaps because of their greater name recognition---further amplifying the cumulative advantage of well-known individuals.
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Highly followed Twitter users tend to receive many more new followers than less-followed users, in particular since Twitter began recommending users to follow in 2010.
“highly followed Twitter users tend to receive many more new followers than less-followed users, in particular since Twitter began recommending users to follow in 2010”
From Stewardship of global collective behavior (2021) · cites this paper as [131]
Link recommendation algorithms have the potential to shape the overall evolution of social networks.
“Link recommendation algorithms have the potential to shape the overall evolution of social networks 120-122 .”
From Machine culture (2023) · cites this paper as [122]
X's 'who to follow' recommendation was observed to disproportionately benefit those users who were already the most popular, fuelling 'the rich get richer' dynamics.
“X's”
From Machine culture (2023) · cites this paper as [122]
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- Authors
- Su, J., Sharma, A., & Goel, S.
- Affiliations
- Stanford University, Stanford, CA, USA
- Twitter (United States) (US)
- Stanford University (US)
- Venue
- Proceedings of the 25th International Conference on World Wide Web
- Published
- 2016-04-11
- Pages
- 1157-1167
- OpenAlex
- W2339299925
- Cited by
- 78
- References
- 37
- Citations by year
- 2026: 6 · 2025: 4 · 2024: 4 · 2023: 10 · 2022: 9 · 2021: 14 · 2020: 6 · 2019: 10 · 2018: 8 · 2017: 5 · 2016: 2
- FWCI
- 11.62
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International World Wide Web Conference (WWW) (publishedVersion)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Social Sciences › Communication › Social Media and Politics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Keywords
- social networking sites, network structure, popularity, rich get richer, Matthew effect, Twitter, network growth dynamics, sublinear growth, name recognition
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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