1 citations · 1 across the 7 of their papers we have counts for
14 papers
Graph Additive Models disentangling feature, topology, and interactions
Clara Alvarez-Rodriguez, Steve Azzolin, Vincenzo Marco De Luca +3
Graph Neural Additive Networks (GNANs) have emerged as effective, interpretable-by-design architectures for graph learning. They achieve transparency by combining per-feature shape…
Evolutionary One-Step Generators: Fast and Diverse Sampling for Discrete Design
Marcus Vukojevic, Erik Nielsen, Veronica Lachi +2
Several discrete design tasks, such as molecular discovery, require diverse collections of useful candidates at low computational cost. High validity alone does not guarantee a use…
Rethinking Epistemic Uncertainty in Node Classification through Information Growth
Emma Meneghini, Francesco Ferrini, Bruno Lepri +2
Epistemic uncertainty should decrease as additional information about the data-generating process (DGP) becomes available to the predictor. Yet, existing graph evidential deep lear…
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…
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…
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…
A Neuro-Symbolic Approach for Probabilistic Reasoning on Graph Data
Raffaele Pojer, Andrea Passerini, Kim G. Larsen +1
Graph neural networks (GNNs) excel at predictive tasks on graph-structured data but often lack the ability to incorporate symbolic domain knowledge and perform general reasoning. R…
Boosting Team Modeling through Tempo-Relational Representation Learning
Vincenzo Marco De Luca, Giovanna Varni, Andrea Passerini
Team modeling remains a fundamental challenge at the intersection of Artificial Intelligence and Social Sciences. Although a variety of computational models have been proposed in t…
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…
Boosting Relational Deep Learning with Pretrained Tabular Models
Veronica Lachi, Antonio Longa, Beatrice Bevilacqua +3
Relational databases, organized into tables connected by primary-foreign key relationships, are a common format for organizing data. Making predictions on relational data often inv…
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…