280 citations · 777 across the 29 of their papers we have counts for
35 papers
Beyond Trained Models: Compiling GNNs for a Sound Explainer Benchmark
Steve Azzolin, Francesco Paolo Nerini, Stefano Teso +4
Explainers for Graph Neural Networks (GNNs) are commonly evaluated by their plausibility, i.e., how well their explanations recover a predefined ground truth, such as a motif plant…
Inductive Correlation Clustering with Graph Neural Networks
Francesco Paolo Nerini, Francesco Bonchi, Arijit Khan +1
Correlation Clustering (CC) is a natural formulation of clustering in combinatorial optimization, which uses a graph representation of the input and does not require a pre-specifie…
The Geometry of Representational Failures in Vision Language Models
Daniele Savietto, Declan Campbell, André Panisson +4
Vision-Language Models (VLMs) exhibit puzzling failures in multi-object visual tasks, such as hallucinating non-existent elements or failing to identify the most similar objects am…
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…
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.…
Multi-Class and Multi-Task Strategies for Neural Directed Link Prediction
Claudio Moroni, Claudio Borile, Carolina Mattsson +2
Link Prediction is a foundational task in Graph Representation Learning, supporting applications like link recommendation, knowledge graph completion and graph generation. Graph Ne…