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cs.LG2026
Edge-Girth as a Structural Edge Feature for Graph Neural Networks
Lilian Marey, Charlotte Laclau
Graph neural networks (GNN) based on message passing are provably no more powerful than the one-dimensional Weisfeiler--Leman colour-refinement test (1-WL): two graphs it cannot te…
cs.LG2026
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…
cs.LG2022★ 1 cited
SimHawNet: A Modified Hawkes Process for Temporal Network Simulation
Mathilde Perez, Raphaël Romero, Bo Kang +3
Temporal networks allow representing connections between objects while incorporating the temporal dimension. While static network models can capture unchanging topological regulari…