3 papers
cs.LG2026
Hierarchical Pooling for Sheaf Neural Networks
Dionisia Naddeo, Carlo Abate, Pietro Liò +2
Sheaf Neural Networks (SNNs) generalize Graph Neural Networks (GNNs) by replacing scalar node signals with stalk-valued signals and by using restriction maps to measure compatibili…
cs.LG2026
Torch Geometric Pool: the PyTorch library for pooling in Graph Neural Networks
Carlo Abate, Ivan Marisca, Filippo Maria Bianchi
Torch Geometric Pool (tgp) is a pooling library built on top of PyTorch Geometric. Graph pooling methods differ in how they assign nodes to supernodes, how they handle batches, wha…
cs.LG2025
MaxCutPool: differentiable feature-aware Maxcut for pooling in graph neural networks
Carlo Abate, Filippo Maria Bianchi
We propose a novel approach to compute the MAXCUT in attributed graphs, i.e., graphs with features associated with nodes and edges. Our approach works well on any kind of graph top…