5 papers
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…
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…
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…
Any2Graph: Deep End-To-End Supervised Graph Prediction With An Optimal Transport Loss
Paul Krzakala, Junjie Yang, Rémi Flamary +3
We propose Any2graph, a generic framework for end-to-end Supervised Graph Prediction (SGP) i.e. a deep learning model that predicts an entire graph for any kind of input. The frame…