49 citations · 144 across the 24 of their papers we have counts for
18 papers · 1 filter
When Missingness Follows the Graph: Beyond Feature Propagation for Missing Features
Enrico Chialà, Francesco Ferrini, Antonio Longa +4
Graph Neural Networks (GNNs) commonly assume that node features are fully observed, despite missing features being pervasive in real-world applications. By leveraging the dependenc…
Towards Actionable Surgical Team Dynamics: from Teamwork to Counterfactual Annotations
Vincenzo Marco De Luca, Antonio Longa, Andrea Passerini
Modeling team interactions in high-stakes environments such as operating rooms is critical for understanding how coordination, communication, and individual behaviors shape team pe…
From Multimodal Observation to Interpretable Suggestions: Counterfactual Time-Expanded Relational Modeling of Surgical Teams
Vincenzo Marco De Luca, Antonio Longa, Giovanna Varni +1
In surgery, patient safety is threatened not only by technical issues but also by poor teamwork. However, existing surgical AI-based solutions focus mainly on visual workflow and t…
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…
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…
A Benchmark Dataset for Graph Regression with Homogeneous and Multi-Relational Variants
Peter Samoaa, Marcus Vukojevic, Morteza Haghir Chehreghani +1
Graph-level regression underpins many real-world applications, yet public benchmarks remain heavily skewed toward molecular graphs and citation networks. This limited diversity hin…
Simple Path Structural Encoding for Graph Transformers
Louis Airale, Antonio Longa, Mattia Rigon +2
Graph transformers extend global self-attention to graph-structured data, achieving notable success in graph learning. Recently, random walk structural encoding (RWSE) has been fou…
A Self-Explainable Heterogeneous GNN for Relational Deep Learning
Francesco Ferrini, Antonio Longa, Andrea Passerini +1
Recently, significant attention has been given to the idea of viewing relational databases as heterogeneous graphs, enabling the application of graph neural network (GNN) technolog…
xAI-Drop: Don't Use What You Cannot Explain
Vincenzo Marco De Luca, Antonio Longa, Pietro Liò +1
Graph Neural Networks (GNNs) have emerged as the predominant paradigm for learning from graph-structured data, offering a wide range of applications from social network analysis to…
Analysing the Behaviour of Tree-Based Neural Networks in Regression Tasks
Peter Samoaa, Mehrdad Farahani, Antonio Longa +2
The landscape of deep learning has vastly expanded the frontiers of source code analysis, particularly through the utilization of structural representations such as Abstract Syntax…
Reconsidering Faithfulness in Regular, Self-Explainable and Domain Invariant GNNs
Steve Azzolin, Antonio Longa, Stefano Teso +1
As Graph Neural Networks (GNNs) become more pervasive, it becomes paramount to build reliable tools for explaining their predictions. A core desideratum is that explanations are \t…
Meta-Path Learning for Multi-relational Graph Neural Networks
Francesco Ferrini, Antonio Longa, Andrea Passerini +1
Existing multi-relational graph neural networks use one of two strategies for identifying informative relations: either they reduce this problem to low-level weight learning, or th…
Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities
Antonio Longa, Veronica Lachi, Gabriele Santin +5
Graph Neural Networks (GNNs) have become the leading paradigm for learning on (static) graph-structured data. However, many real-world systems are dynamic in nature, since the grap…
Explaining the Explainers in Graph Neural Networks: a Comparative Study
Antonio Longa, Steve Azzolin, Gabriele Santin +4
Following a fast initial breakthrough in graph based learning, Graph Neural Networks (GNNs) have reached a widespread application in many science and engineering fields, prompting…
Global Explainability of GNNs via Logic Combination of Learned Concepts
Steve Azzolin, Antonio Longa, Pietro Barbiero +2
While instance-level explanation of GNN is a well-studied problem with plenty of approaches being developed, providing a global explanation for the behaviour of a GNN is much less…
Generating Synthetic Mobility Networks with Generative Adversarial Networks
Giovanni Mauro, Massimiliano Luca, Antonio Longa +2
The increasingly crucial role of human displacements in complex societal phenomena, such as traffic congestion, segregation, and the diffusion of epidemics, is attracting the inter…