Link recommendations: Their impact on network structure and minorities

ACM Web Science Conference (WebSci), pp. 228–238

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.

Authors 4

1. Antonio Ferrara Aachen GESIS - Leibniz Institute for the Social Sciences

   GESIS - Leibniz Institute for the Social Sciences · RWTH Aachen University

   Affiliation as printed

   GESIS Leibniz Institute for the Social Sciences, Germany and RWTH Aachen University, Germany

2. Lisette Espín-Noboa

   Central European University · Complexity Science Hub

   Affiliation as printed

   Complexity Science Hub, Austria and Central European University, Austria

3. Fariba Karimi

   Complexity Science Hub

   Affiliation as printed

   Complexity Science Hub, Austria

4. Claudia Wagner Aachen GESIS - Leibniz Institute for the Social Sciences

   GESIS - Leibniz Institute for the Social Sciences · RWTH Aachen University

   Affiliation as printed

   GESIS Leibniz Institute for the Social Sciences, Germany and RWTH Aachen University, Germany