78 citations · 168 across the 9 of their papers we have counts for
9 papers · 1 filter
Measuring What Matters: A Unified Evaluation Framework for GNN Explainability
Francesco Paolo Nerini, Mirko Zaffaroni, Paolo Baracco +2
Graph eXplainable AI (G-XAI) is increasingly important for making Graph Neural Networks interpretable and accountable. While a growing number of explainers are available, choosing…
Relational Concept Bottleneck Models
Pietro Barbiero, Francesco Giannini, Gabriele Ciravegna +2
The design of interpretable deep learning models working in relational domains poses an open challenge: interpretable deep learning methods, such as Concept Bottleneck Models (CBMs…
Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off
Mateo Espinosa Zarlenga, Pietro Barbiero, Gabriele Ciravegna +9
Deploying AI-powered systems requires trustworthy models supporting effective human interactions, going beyond raw prediction accuracy. Concept bottleneck models promote trustworth…
Encoding Concepts in Graph Neural Networks
Lucie Charlotte Magister, Pietro Barbiero, Dmitry Kazhdan +5
The opaque reasoning of Graph Neural Networks induces a lack of human trust. Existing graph network explainers attempt to address this issue by providing post-hoc explanations, how…
Knowledge-driven Active Learning
Gabriele Ciravegna, Frédéric Precioso, Alessandro Betti +2
The deployment of Deep Learning (DL) models is still precluded in those contexts where the amount of supervised data is limited. To answer this issue, active learning strategies ai…
Graph Neural Networks for Graph Drawing
Matteo Tiezzi, Gabriele Ciravegna, Marco Gori
Graph Drawing techniques have been developed in the last few years with the purpose of producing aesthetically pleasing node-link layouts. Recently, the employment of differentiabl…