22 citations · 79 across the 17 of their papers we have counts for
19 papers
A City-Scale Dataset of Traffic Flows, Travel Times, and Urban Context
Riccardo Cappi, Massimiliano Luca, Pietro Fontolan +3
We present a multi-source traffic dataset derived from Automatic Vehicle Identification (AVI) recordings in Padua, Italy, spanning from February 2026 to August 2026. The dataset co…
The Unreasonable Effectiveness of Randomized Representations in Online Continual Graph Learning
Giovanni Donghi, Daniele Zambon, Luca Pasa +2
Catastrophic forgetting is one of the main obstacles for Online Continual Graph Learning (OCGL), where nodes arrive one by one, distribution drifts may occur at any time and offlin…
Iterative In-Context Learning to Enhance LLMs Abstract Reasoning: The Case-Study of Algebraic Tasks
Stefano Fioravanti, Matteo Zavatteri, Roberto Confalonieri +4
LLMs face significant challenges in systematic generalization, particularly when dealing with reasoning tasks requiring compositional rules and handling out-of-distribution example…
Discovering Generalizable Governing Equations for Graph Dynamical Systems with Interpretable Neural Networks
Riccardo Cappi, Paolo Frazzetto, Nicolò Navarin +1
The discovery of symbolic governing equations is a central goal in science; yet, it remains challenging particularly for graph dynamical systems, where the network topology further…
Online Continual Graph Learning
Giovanni Donghi, Luca Pasa, Daniele Zambon +2
Continual Learning (CL) aims to incrementally acquire new knowledge while mitigating catastrophic forgetting. Within this setting, Online Continual Learning (OCL) focuses on updati…
RGCVAE: Relational Graph Conditioned Variational Autoencoder for Molecule Design
Davide Rigoni, Nicolò Navarin, Alessandro Sperduti
Identifying molecules that exhibit some pre-specified properties is a difficult problem to solve. In the last few years, deep generative models have been used for molecule generati…
An Explainable Decision Support System for Predictive Process Analytics
Riccardo Galanti, Massimiliano de Leoni, Merylin Monaro +4
Predictive Process Analytics is becoming an essential aid for organizations, providing online operational support of their processes. However, process stakeholders need to be provi…
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…
LSTM Networks for Data-Aware Remaining Time Prediction of Business Process Instances
Nicolò Navarin, Beatrice Vincenzi, Mirko Polato +1
Predicting the completion time of business process instances would be a very helpful aid when managing processes under service level agreement constraints. The ability to know in a…
Graph Kernels exploiting Weisfeiler-Lehman Graph Isomorphism Test Extensions
Giovanni Da San Martino, Nicolò Navarin, Alessandro Sperduti
In this paper we present a novel graph kernel framework inspired the by the Weisfeiler-Lehman (WL) isomorphism tests. Any WL test comprises a relabelling phase of the nodes based o…
A tree-based kernel for graphs with continuous attributes
Giovanni Da San Martino, Nicolò Navarin, Alessandro Sperduti
The availability of graph data with node attributes that can be either discrete or real-valued is constantly increasing. While existing kernel methods are effective techniques for…
Ordered Decompositional DAG Kernels Enhancements
Giovanni Da San Martino, Nicolò Navarin, Alessandro Sperduti
In this paper, we show how the Ordered Decomposition DAGs (ODD) kernel framework, a framework that allows the definition of graph kernels from tree kernels, allows to easily define…
Extending local features with contextual information in graph kernels
Nicolò Navarin, Alessandro Sperduti, Riccardo Tesselli
Graph kernels are usually defined in terms of simpler kernels over local substructures of the original graphs. Different kernels consider different types of substructures. However,…
An Empirical Study on Budget-Aware Online Kernel Algorithms for Streams of Graphs
Giovanni Da San Martino, Nicolò Navarin, Alessandro Sperduti
Kernel methods are considered an effective technique for on-line learning. Many approaches have been developed for compactly representing the dual solution of a kernel method when…