Showing cs.LGShow all
2 papers · 1 filter
cs.LG2026
Implicit Regularization of Mini-Batch Training in Graph Neural Networks
Clement Wang, Antoine Vialle, Robin Vaysse +1
Mini-batch training of Graph Neural Networks (GNNs) is fundamentally different from training on i.i.d. data: sampling a subgraph alters the topology and introduces boundary effects…
cs.LG2024
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…