11 papers
Benchmarking Sheaf Neural Networks for Inductive Tasks
Stefano Fiorini, Edoardo Coppola, Pietro Liò
Sheaf Neural Networks (SNNs) generalize message passing by replacing scalar edge weights of standard Graph Neural Networks (GNNs) with learnable, edge-dependent restriction maps be…
Heterogeneous Sheaf Neural Networks
Luke Braithwaite, Alessio Borgi, Gabriele Onorato +4
Heterogeneous graphs, whose nodes and edges can belong to different types and feature spaces, arise in many real-world domains, including biology, recommendation, social networks,…
Graph Transductive Sharpening: Leveraging Unlabeled Predictions in Node Classification
Brown Zaz, Mar Gonzà lez I CatalÃ, Ferran Hernandez Caralt +2
In the transductive setting, where the full graph is observed but node labels are only partially available, progress in semi-supervised node classification has largely focused on a…
Hypergraph Pattern Machine: Compositional Tokenization for Higher-Order Interactions
Kyrie Zhao, Zehong Wang, Tianyi Ma +5
Hypergraphs model higher-order relations that drive real-world decisions, from drug prescriptions to recommendations. A central structural signal in such data, beyond what pairwise…
Retrieval-Augmented Generation for Predicting Cellular Responses to Gene Perturbation
Andrea Giuseppe Di Francesco, Andrea Rubbi, Pietro Liò
Predicting how cells respond to genetic perturbations is fundamental to understanding gene function, disease mechanisms, and therapeutic development. While recent deep learning app…
Superposition in Graph Neural Networks
Lukas Pertl, Han Xuanyuan, Pietro Liò
Interpreting graph neural networks (GNNs) is difficult because message passing mixes signals and internal channels rarely align with human concepts. We study superposition, the sha…