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Systematic Biases in Link Prediction: Comparing Heuristic and Graph Embedding Based Methods

  • Aakash Sinha
  • Rémy Cazabet
  • Rémi Vaudaine
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 812)

Abstract

Link prediction is a popular research topic in network analysis. In the last few years, new techniques based on graph embedding have emerged as a powerful alternative to heuristics. In this article, we study the problem of systematic biases in the prediction, and show that some methods based on graph embedding offer less biased results than those based on heuristics, despite reaching lower scores according to usual quality scores. We discuss the relevance of this finding in the context of the filter bubble problem and the algorithmic fairness of recommender systems.

Keywords

Graph embedding Link prediction Systematic biases Filter bubble 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringI.I.T. DelhiNew DelhiIndia
  2. 2.University of Lyon, UCBL, CNRS, LIRIS UMR 5205LyonFrance
  3. 3.University of Lyon, UJM-Saint-Etienne, CNRSSaint-EtienneFrance

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