5 papers
On the Token Collapse Phenomenon in Textual Graph Soft Prompting
Andrea Giuseppe Di Francesco, Rishabh Jain, Franco Maria Nardini +3
Recent methods for injecting graph structure into LLM prompts include graph soft prompting, in which a graph neural network (GNN) learns graph-specific tokens that capture relation…
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…
Differentiable Retrieval-Augmented Generation for Predicting Cellular Responses to Gene Perturbation
Andrea Giuseppe Di Francesco, Andrea Rubbi, Pietro Liò +1
Predicting transcriptional responses to genetic perturbations is fundamental to functional genomics and therapeutic discovery. Recent deep learning models have shown promise in sin…
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…