The outbreak of the Covid-19 pandemic led several governments to impose restrictions on the export of medical supplies. Despite being at odds with the canonical prescriptions of …
From physics to engineering, biology and social science, natural and artificial systems are characterized by interconnected topologies whose features – e.g., heterogeneous …
Graph Neural Networks (GNNs) have been widely used to learn representations on graphs and tackle many real-world problems from a wide range of domains. In this paper we propose …
Networks are a powerful tool to model complex systems, and the definition of many Graph Neural Networks (GNN), Deep Learning algorithms that can handle networks, has opened a new …
Networks measurement is essential to catch and quantify their features, behaviour and/or emerging phenomena. The goal of cohesiveness metric introduced here is to establish the …
Combinatorial optimization problems arise in a wide range of applications from diverse domains. Many of these problems are NP-hard and designing efficient heuristics for them …
Basics of PageRank algorithm have been widely adopted in its variations, tailored for specific scenarios. In this work, we consider the Black Hole metric, an extension of the …
Choosing an effective yet efficient solution to the link building problem means to select which nodes in a network should point a newcomer in order to increase its rank while …
Many systems are today modelled as complex networks, since this representation has been proven being an effective approach for understanding and controlling many real-world …
We propose a multi-stage learning approach for pruning the search space of maximum clique enumeration, a fundamental computationally difficult problem arising in various network …