Link recommendations: Their impact on network structure and minorities
Open accessAbstract
Network-based people recommendation algorithms are widely employed on the Web to suggest new connections in social media or professional platforms. While such recommendations bring people together, the feedback loop between the algorithms and the changes in network structure may exacerbate social biases. These biases include rich-get-richer effects, filter bubbles, and polarization. However, social networks are diverse complex systems and recommendations may affect them differently, depending on their structural properties. In this work, we explore five people recommendation algorithms by systematically applying them over time to different synthetic networks. In particular, we measure to what extent these recommendations change the structure of bi-populated networks and show how these changes affect the minority group.
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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 [121]
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Self-citation
- Authors
- Ferrara, A., Espin-Noboa, L., Karimi, F., & Wagner, C.
- Affiliations
- GESIS - Leibniz Institute for the Social Sciences (DE)
- Complexity Science Hub, Austria and Central European University, Austria
- Complexity Science Hub (AT)
- Venue
- 14th ACM Web Science Conference 2022
- Publisher
- ACM
- Published
- 2022-06-26
- Pages
- 228-238
- OpenAlex
- W4280495998
- Cited by
- 22
- References
- 37
- Citations by year
- 2026: 4 · 2025: 3 · 2024: 6 · 2023: 7 · 2022: 2
- FWCI
- 8.10
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- ACM Web Science Conference (WebSci) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- Österreichische Forschungsförderungsgesellschaft (873927)
- European Union?s Horizon 2020 (860630)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Keywords
- link recommendation, network structure, social bias, rich-get-richer effect, filter bubbles, polarization, minority groups, bipartite graphs, synthetic networks, social media
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