Systematic Biases in Link Prediction: comparing heuristic and graph embedding based methods

Aakash Sinha (1) , Rémy Cazabet (2, 3, 4, 5) , Rémi Vaudaine (4)

1 IIT Delhi - Indian Institute of Technology Delhi
2 DM2L - Data Mining and Machine Learning
3 IXXI - Institut Rhône-Alpin des systèmes complexes
4 LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
5 UCBL - Université Claude Bernard Lyon 1

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: link prediction; systematic biases; filter bubble; graph embedding

Domains: Social and Information Networks [cs.SI]

Aakash Sinha, Rémy Cazabet, Rémi Vaudaine. Systematic Biases in Link Prediction: comparing heuristic and graph embedding based methods. Complex networks 2018 - The 7th International Conference on Complex Networks and Their Applications, 2018, Cambridge, United Kingdom. ⟨10.1007/978-3-030-05411-3_7⟩. ⟨hal-01892997v2⟩