11 papers
Overcoming Shortcut Learning in Graph Neural Networks through Active Explanation Guidance
Taraneh Younesian, Steve Azzolin, Antonio Longa +3
Graph Neural Networks (GNNs) can solve prediction tasks by unintentionally exploiting shortcuts---that is, edges, nodes, and features that correlate with but are not causal for the…
Rethinking GNNs and Missing Features: Challenges, Evaluation and a Robust Solution
Francesco Ferrini, Veronica Lachi, Antonio Longa +5
Handling missing node features is a key challenge for deploying Graph Neural Networks (GNNs) in real-world domains such as healthcare and sensor networks. Existing studies mostly a…
Actionable Real-Time Modeling of Surgical Team Dynamics via Time-Expanded Interaction Graphs
Vincenzo Marco De Luca, Antonio Longa, Giovanna Varni +1
Surgical team performance arises from complex interactions between technical execution and non-technical skills, including communication and coordination dynamics. However, current…
GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction
Francesco Ferrini, Veronica Lachi, Antonio Longa +2
Graph Neural Networks (GNNs) often struggle to capture the link-specific structural patterns crucial for accurate link prediction, as their node-centric message-passing schemes ove…
Bridging Theory and Practice in Link Representation with Graph Neural Networks
Veronica Lachi, Francesco Ferrini, Antonio Longa +3
Graph Neural Networks (GNNs) are widely used to compute representations of node pairs for downstream tasks such as link prediction. Yet, theoretical understanding of their expressi…
Community Aware Temporal Network Generation
Nicolò Alessandro Girardini, Antonio Longa, Gaia Trebucchi +3
The advantages of temporal networks in capturing complex dynamics, such as diffusion and contagion, has led to breakthroughs in real world systems across numerous fields. In the ca…