186 citations · 205 across the 15 of their papers we have counts for
4 papers · 1 filter
k-hop Fairness: Addressing Disparities in Graph Link Prediction Beyond First-Order Neighborhoods
Lilian Marey, Tiphaine Viard, Charlotte Laclau
Link prediction (LP) plays a central role in graph-based applications, particularly in social recommendation. However, real-world graphs often reflect structural biases, most notab…
Structural Bias Beyond Homophily: A Study of Fairness in Link Prediction
Lilian Marey, Mathilde Perez, Tiphaine Viard +1
Graph link prediction (LP) plays a critical role in socially impactful applications such as job recommendation and friendship formation, making fairness a critical concern in this…
Graph as a feature: improving node classification with non-neural graph-aware logistic regression
Simon Delarue, Thomas Bonald, Tiphaine Viard
Graph Neural Networks (GNNs) and their message passing framework that leverages both structural and feature information, have become a standard method for solving graph-based machi…
Classifying Wikipedia in a fine-grained hierarchy: what graphs can contribute
Tiphaine Viard, Thomas McLachlan, Hamidreza Ghader +1
Wikipedia is a huge opportunity for machine learning, being the largest semi-structured base of knowledge available. Because of this, many works examine its contents, and focus on…