Link recommendations: Their impact on network structure and minorities
Antonio Ferrara, Lisette Esp\'in-Noboa, Fariba Karimi, Claudia, Wagner

TL;DR
This paper systematically analyzes how different link recommendation algorithms influence social network structures and minority groups, revealing potential biases and effects on minority visibility, especially in heterophilic networks.
Contribution
It provides a comparative study of five recommendation algorithms on synthetic networks, highlighting their structural impacts and implications for minority groups.
Findings
All algorithms increase network cohesion and close triangles.
Most algorithms favor nodes with high in-degree.
Recommendations can reduce minority visibility in heterophilic networks.
Abstract
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. Our systematic experimentation helps to better understand when link…
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