In graph neural networks (GNNs), pooling operators compute local summaries of input
graphs to capture their global properties, and they are fundamental for building deep GNNs …

In this paper we propose a frameworks for identifying patterns and regularities in the pseudo-
anonymized Call Data Records (CDR) pertaining a generic subscriber of a mobile operator …

In recent years, graph-based data mining (GDM) is the most accepted research due to
numerous applications in a broad selection of software bug localization, computational …

We propose a system able to synthesize automatically a classification model and a set of
interpretable decision rules defined over a set of symbols, corresponding to frequent …

Due to the increasing amount of sensors and data streams that can be collected in order to
monitor electric distribution networks, developing predictive diagnostic systems over Smart …

In this paper we propose a novel evolutive agent-based clustering algorithm where agents
act as individuals of an evolving population, each one performing a random walk on a …

Discovering regularities in Big Data is nowadays a crucial task in many different
applications, from bioinformatics to cybersecurity. To this aim, a promising approach consists …

Multi-agent systems recently gained a lot of attention for solving machine learning and data
mining problems. Furthermore, their peculiar divide-and-conquer approach is appealing …

This thesis explores the field of graph neural networks, a class of deep learning models
designed to learn representations of graphs. We organise the work into two parts. In the first …

With the development of industrial big data, it has become an important research direction to
use combinatorial optimization to coordinate multi-objective problems in complex …