15 citations · 19 across the 5 of their papers we have counts for
5 papers
A method for the systematic generation of graph XAI benchmarks via Weisfeiler-Leman coloring
Michele Fontanesi, Alessio Micheli, Marco Podda +1
Graph neural networks have become the de facto model for learning from structured data. However, the decision-making process of GNNs remains opaque to the end user, which undermine…
Efficient quantification on large-scale networks
Alessio Micheli, Alejandro Moreo, Marco Podda +3
Network quantification (NQ) is the problem of estimating the proportions of nodes belonging to each class in subsets of unlabelled graph nodes. When prior probability shift is at p…
An Empirical Evaluation of Rewiring Approaches in Graph Neural Networks
Alessio Micheli, Domenico Tortorella
Graph neural networks compute node representations by performing multiple message-passing steps that consist in local aggregations of node features. Having deep models that can lev…
Addressing Heterophily in Node Classification with Graph Echo State Networks
Alessio Micheli, Domenico Tortorella
Node classification tasks on graphs are addressed via fully-trained deep message-passing models that learn a hierarchy of node representations via multiple aggregations of a node's…
Beyond Homophily with Graph Echo State Networks
Domenico Tortorella, Alessio Micheli
Graph Echo State Networks (GESN) have already demonstrated their efficacy and efficiency in graph classification tasks. However, semi-supervised node classification brought out the…