4 papers
Homophily-aware Supervised Contrastive Counterfactual Augmented Fair Graph Neural Network
Mahdi Tavassoli Kejani, Fadi Dornaika, Charlotte Laclau +1
In recent years, Graph Neural Networks (GNNs) have achieved remarkable success in tasks such as node classification, link prediction, and graph representation learning. However, th…
How Predicted Links Influence Network Evolution: Disentangling Choice and Algorithmic Feedback in Dynamic Graphs
Mathilde Perez, Raphaël Romero, Jefrey Lijffijt +1
Link prediction models are increasingly used to recommend interactions in evolving networks, yet their impact on network structure is typically assessed from static snapshots. In p…
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…
The quest for the GRAph Level autoEncoder (GRALE)
Paul Krzakala, Gabriel Melo, Charlotte Laclau +2
Although graph-based learning has attracted a lot of attention, graph representation learning is still a challenging task whose resolution may impact key application fields such as…