Link recommendations: Their impact on network structure and minorities

Antonio Ferrara, Lisette Espin-Noboa, Fariba Karimi, Claudia Wagner

Research output: Contribution to Book/Report typesConference contributionpeer-review

Abstract (may include machine translation)

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 recommendation algorithms are beneficial or harmful to minority groups in social networks. In particular, our findings suggest that, while all algorithms tend to close triangles and increase cohesion, all algorithms except Node2Vec are prone to favor and suggest nodes with high in-degree. Furthermore, we found that, especially when both classes are heterophilic, recommendation algorithms can reduce the visibility of minorities.

Original languageEnglish
Title of host publicationWebSci 2022 - Proceedings of the 14th ACM Web Science Conference
PublisherAssociation for Computing Machinery
Pages228-238
Number of pages11
ISBN (Electronic)9781450391917
DOIs
StatePublished - 26 Jun 2022
Event14th ACM Web Science Conference, WebSci 2022 - Virtual, Online, Spain
Duration: 26 Jun 202229 Jun 2022

Publication series

NameACM International Conference Proceeding Series

Conference

Conference14th ACM Web Science Conference, WebSci 2022
Country/TerritorySpain
CityVirtual, Online
Period26/06/2229/06/22

Keywords

  • Recommendation algorithms
  • friendship recommendations
  • homophily
  • network science
  • preferential attachment.
  • social networks

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