5 citations · 11 across the 9 of their papers we have counts for
14 papers · 1 filter
A Spectral Interpretation of Redundancy in a Graph Reservoir
Anna Bison, Alessandro Sperduti
Reservoir computing has been successfully applied to graphs as a preprocessing method to improve the training efficiency of Graph Neural Networks (GNNs). However, a common issue th…
Exact Computation of Any-Order Shapley Interactions for Graph Neural Networks
Maximilian Muschalik, Fabian Fumagalli, Paolo Frazzetto +5
Albeit the ubiquitous use of Graph Neural Networks (GNNs) in machine learning (ML) prediction tasks involving graph-structured data, their interpretability remains challenging. In…
IFH: a Diffusion Framework for Flexible Design of Graph Generative Models
Samuel Cognolato, Alessandro Sperduti, Luciano Serafini
Graph generative models can be classified into two prominent families: one-shot models, which generate a graph in one go, and sequential models, which generate a graph by successiv…
Simple Graph Convolutional Networks
Luca Pasa, Nicolò Navarin, Wolfgang Erb +1
Many neural networks for graphs are based on the graph convolution operator, proposed more than a decade ago. Since then, many alternative definitions have been proposed, that tend…
Short-Term Memory Optimization in Recurrent Neural Networks by Autoencoder-based Initialization
Antonio Carta, Alessandro Sperduti, Davide Bacciu
Training RNNs to learn long-term dependencies is difficult due to vanishing gradients. We explore an alternative solution based on explicit memorization using linear autoencoders f…
Conditional Constrained Graph Variational Autoencoders for Molecule Design
Davide Rigoni, Nicolò Navarin, Alessandro Sperduti
In recent years, deep generative models for graphs have been used to generate new molecules. These models have produced good results, leading to several proposals in the literature…