4 papers
Early-Exit Graph Neural Networks for Link Prediction
Roman Knyazhitskiy, Andrea Giuseppe Di Francesco
Graph Neural Networks are great for link prediction in various network-like structures; however, the question of their speed/quality tradeoff has been barely studied. While in prac…
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…
Cross-Document Neural Re-Ranking via Query-Induced Subgraphs
Andrea Giuseppe Di Francesco, Christian Giannetti, Nicola Tonellotto +1
Neural re-rankers typically score query-document pairs independently, neglecting cross-document context within the retrieved candidate set. We propose Graph Neural Re-Ranking (GNRR…
Link Prediction with Physics-Inspired Graph Neural Networks
Andrea Giuseppe Di Francesco, Francesco Caso, Maria Sofia Bucarelli +1
The message-passing mechanism underlying Graph Neural Networks (GNNs) is not naturally suited for heterophilic datasets, where adjacent nodes often have different labels. Most solu…