3 papers
cs.LG2026
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…
cs.LG2026
Size-adaptive Hypothesis Testing for Fairness
Antonio Ferrara, Francesco Cozzi, Alan Perotti +2
Determining whether an algorithmic decision-making system discriminates against a specific demographic typically involves comparing a single point estimate of a fairness metric aga…
cs.AI2025
Learning Individual Behavior in Agent-Based Models with Graph Diffusion Networks
Francesco Cozzi, Marco Pangallo, Alan Perotti +2
Agent-Based Models (ABMs) are powerful tools for studying emergent properties in complex systems. In ABMs, agent behaviors are governed by local interactions and stochastic rules.…