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cs.LG2025
Pattern-Based Graph Classification: Comparison of Quality Measures and Importance of Preprocessing
Lucas Potin, Rosa Figueiredo, Vincent Labatut +1
Graph classification aims to categorize graphs based on their structural and attribute features, with applications in diverse fields such as social network analysis and bioinformat…
cs.LG2025
FedSV: Byzantine-Robust Federated Learning via Shapley Value
Khaoula Otmani, Rachid Elazouzi, Vincent Labatut
In Federated Learning (FL), several clients jointly learn a machine learning model: each client maintains a local model for its local learning dataset, while a master server mainta…
cs.LG2024
Whole-Graph Representation Learning For the Classification of Signed Networks
Noé Cecillon, Vincent Labatut, Richard Dufour +1
Graphs are ubiquitous for modeling complex systems involving structured data and relationships. Consequently, graph representation learning, which aims to automatically learn low-d…