5 citations · 6 across the 4 of their papers we have counts for
7 papers
Object-centric Process Predictive Analytics
Riccardo Galanti, Massimiliano de Leoni, Nicolò Navarin +1
Object-centric processes (a.k.a. Artifact-centric processes) are implementations of a paradigm where an instance of one process is not executed in isolation but interacts with othe…
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…
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…
A Systematic Assessment of Deep Learning Models for Molecule Generation
Davide Rigoni, Nicolò Navarin, Alessandro Sperduti
In recent years the scientific community has devoted much effort in the development of deep learning models for the generation of new molecules with desirable properties (i.e. drug…
On Filter Size in Graph Convolutional Networks
Dinh Van Tran, Nicolò Navarin, Alessandro Sperduti
Recently, many researchers have been focusing on the definition of neural networks for graphs. The basic component for many of these approaches remains the graph convolution idea p…
Pre-training Graph Neural Networks with Kernels
Nicolò Navarin, Dinh V. Tran, Alessandro Sperduti
Many machine learning techniques have been proposed in the last few years to process data represented in graph-structured form. Graphs can be used to model several scenarios, from…