4 citations · 4 across the 4 of their papers we have counts for
5 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…
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…
Ethics in rotten apples: A network epidemiology approach for active cyber defense
Francesco Bonacina, Ignacio Echegoyen, Diego Escribano +5
As Internet of Things (IoT) technology grows, so does the threat of malware infections. A proposed countermeasure, the use of benevolent "white worms" to combat malicious "black wo…
Fast and Effective GNN Training through Sequences of Random Path Graphs
Francesco Bonchi, Claudio Gentile, Francesco Paolo Nerini +2
We present GERN, a novel scalable framework for training GNNs in node classification tasks, based on effective resistance, a standard tool in spectral graph theory. Our method prog…